text:
Autocalibration method for non-stationary CT bias correction.
Vegas-Sánchez-Ferrero, Gonzalo; Ledesma-Carbayo, Maria J; Washko, George R; Estépar, Raúl San José
Computed tomography (CT) is a widely used imaging modality for screening and diagnosis. However, the deleterious effects of radiation exposure inherent in CT imaging require the development of image reconstruction methods which can reduce exposure levels. The development of iterative reconstruction techniques is now enabling the acquisition of low-dose CT images whose quality is comparable to that of CT images acquired with much higher radiation dosages. However, the characterization and calibration of the CT signal due to changes in dosage and reconstruction approaches is crucial to provide clinically relevant data. Although CT scanners are calibrated as part of the imaging workflow, the calibration is limited to select global reference values and does not consider other inherent factors of the acquisition that depend on the subject scanned (e.g. photon starvation, partial volume effect, beam hardening) and result in a non-stationary noise response. In this work, we analyze the effect of reconstruction biases caused by non-stationary noise and propose an autocalibration methodology to compensate it. Our contributions are: 1) the derivation of a functional relationship between observed bias and non-stationary noise, 2) a robust and accurate method to estimate the local variance, 3) an autocalibration methodology that does not necessarily rely on a calibration phantom, attenuates the bias caused by noise and removes the systematic bias observed in devices from different vendors. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed the suitability of the proposed methods for removing the intra-device and inter-device reconstruction biases. Copyright © 2017 Elsevier B.V. All rights reserved.
A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield.
Ringard, Justine; Seyler, Frederique; Linguet, Laurent
Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.
Evaluation of bias-correction methods for ensemble streamflow volume forecasts
Directory of Open Access Journals (Sweden)
T. Hashino
Full Text Available Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa, issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.
RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip.
Xu, Zongli; Langie, Sabine A S; De Boever, Patrick; Taylor, Jack A; Niu, Liang
The Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels. Here we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html ). RELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.
Bias-correction of CORDEX-MENA projections using the Distribution Based Scaling method
Bosshard, Thomas; Yang, Wei; Sjökvist, Elin; Arheimer, Berit; Graham, L. Phil
Within the Regional Initiative for the Assessment of the Impact of Climate Change on Water Resources and Socio-Economic Vulnerability in the Arab Region (RICCAR) lead by UN ESCWA, CORDEX RCM projections for the Middle East Northern Africa (MENA) domain are used to drive hydrological impacts models. Bias-correction of newly available CORDEX-MENA projections is a central part of this project. In this study, the distribution based scaling (DBS) method has been applied to 6 regional climate model projections driven by 2 RCP emission scenarios. The DBS method uses a quantile mapping approach and features a conditional temperature correction dependent on the wet/dry state in the climate model data. The CORDEX-MENA domain is particularly challenging for bias-correction as it spans very diverse climates showing pronounced dry and wet seasons. Results show that the regional climate models simulate too low temperatures and often have a displaced rainfall band compared to WATCH ERA-Interim forcing data in the reference period 1979-2008. DBS is able to correct the temperature biases as well as some aspects of the precipitation biases. Special focus is given to the analysis of the influence of the dry-frequency bias (i.e. climate models simulating too few rain days) on the bias-corrected projections and on the modification of the climate change signal by the DBS method.
A method of bias correction for maximal reliability with dichotomous measures.
Penev, Spiridon; Raykov, Tenko
This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.
Regression dilution bias: tools for correction methods and sample size calculation.
Berglund, Lars
Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.
N3 Bias Field Correction Explained as a Bayesian Modeling Method
DEFF Research Database (Denmark)
Larsen, Christian Thode; Iglesias, Juan Eugenio; Van Leemput, Koen
Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function. In t...
A brain MRI bias field correction method created in the Gaussian multi-scale space
Chen, Mingsheng; Qin, Mingxin
A pre-processing step is needed to correct for the bias field signal before submitting corrupted MR images to such image-processing algorithms. This study presents a new bias field correction method. The method creates a Gaussian multi-scale space by the convolution of the inhomogeneous MR image with a two-dimensional Gaussian function. In the multi-Gaussian space, the method retrieves the image details from the differentiation of the original image and convolution image. Then, it obtains an image whose inhomogeneity is eliminated by the weighted sum of image details in each layer in the space. Next, the bias field-corrected MR image is retrieved after the Υ correction, which enhances the contrast and brightness of the inhomogeneity-eliminated MR image. We have tested the approach on T1 MRI and T2 MRI with varying bias field levels and have achieved satisfactory results. Comparison experiments with popular software have demonstrated superior performance of the proposed method in terms of quantitative indices, especially an improvement in subsequent image segmentation.
Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods
Hakala, Kirsti; Addor, Nans; Seibert, Jan
Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of
A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS Soil Moisture: Retrieval Ensembles
Directory of Open Access Journals (Sweden)
Ju Hyoung Lee
Full Text Available Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV bias correction method for Soil Moisture and Ocean Salinity (SMOS soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration. In contrast, the existing method of Cumulative Distribution Function (CDF matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area and Niger (dry and sandy bare soils, it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach.
A New Online Calibration Method Based on Lord's Bias-Correction.
He, Yinhong; Chen, Ping; Li, Yong; Zhang, Shumei
Online calibration technique has been widely employed to calibrate new items due to its advantages. Method A is the simplest online calibration method and has attracted many attentions from researchers recently. However, a key assumption of Method A is that it treats person-parameter estimates θ ^ s (obtained by maximum likelihood estimation [MLE]) as their true values θ s , thus the deviation of the estimated θ ^ s from their true values might yield inaccurate item calibration when the deviation is nonignorable. To improve the performance of Method A, a new method, MLE-LBCI-Method A, is proposed. This new method combines a modified Lord's bias-correction method (named as maximum likelihood estimation-Lord's bias-correction with iteration [MLE-LBCI]) with the original Method A in an effort to correct the deviation of θ ^ s which may adversely affect the item calibration precision. Two simulation studies were carried out to explore the performance of both MLE-LBCI and MLE-LBCI-Method A under several scenarios. Simulation results showed that MLE-LBCI could make a significant improvement over the ML ability estimates, and MLE-LBCI-Method A did outperform Method A in almost all experimental conditions.
A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction.
Chang, Huibin; Huang, Weimin; Wu, Chunlin; Huang, Su; Guan, Cuntai; Sekar, Sakthivel; Bhakoo, Kishore Kumar; Duan, Yuping
Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L 0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.
Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean
MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
Temperature effects on pitfall catches of epigeal arthropods: a model and method for bias correction.
Saska, Pavel; van der Werf, Wopke; Hemerik, Lia; Luff, Martin L; Hatten, Timothy D; Honek, Alois; Pocock, Michael
Carabids and other epigeal arthropods make important contributions to biodiversity, food webs and biocontrol of invertebrate pests and weeds. Pitfall trapping is widely used for sampling carabid populations, but this technique yields biased estimates of abundance ('activity-density') because individual activity - which is affected by climatic factors - affects the rate of catch. To date, the impact of temperature on pitfall catches, while suspected to be large, has not been quantified, and no method is available to account for it. This lack of knowledge and the unavailability of a method for bias correction affect the confidence that can be placed on results of ecological field studies based on pitfall data.Here, we develop a simple model for the effect of temperature, assuming a constant proportional change in the rate of catch per °C change in temperature, r , consistent with an exponential Q 10 response to temperature. We fit this model to 38 time series of pitfall catches and accompanying temperature records from the literature, using first differences and other detrending methods to account for seasonality. We use meta-analysis to assess consistency of the estimated parameter r among studies.The mean rate of increase in total catch across data sets was 0·0863 ± 0·0058 per °C of maximum temperature and 0·0497 ± 0·0107 per °C of minimum temperature. Multiple regression analyses of 19 data sets showed that temperature is the key climatic variable affecting total catch. Relationships between temperature and catch were also identified at species level. Correction for temperature bias had substantial effects on seasonal trends of carabid catches. Synthesis and Applications . The effect of temperature on pitfall catches is shown here to be substantial and worthy of consideration when interpreting results of pitfall trapping. The exponential model can be used both for effect estimation and for bias correction of observed data. Correcting for temperature
Combination of biased forecasts: Bias correction or bias based weights?
Wenzel, Thomas
Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts, and on the other we calculate bias based weights. A simulation study gives some insight in the situations where we should use the different methods.
An improved level set method for brain MR images segmentation and bias correction.
Chen, Yunjie; Zhang, Jianwei; Macione, Jim
Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.
Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.
Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter
With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.
Evaluation of Bias Correction Method for Satellite-Based Rainfall Data
Directory of Open Access Journals (Sweden)
Haris Akram Bhatti
Full Text Available With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA Climate Prediction Centre (CPC morphing technique (CMORPH satellite rainfall product (CMORPH in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW sizes and for sequential windows (SW's of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE. To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r and standard deviation (SD. Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.
Evaluation of Bias Correction Method for Satellite-Based Rainfall Data
Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter
With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363
Bias correction for estimated QTL effects using the penalized maximum likelihood method.
Zhang, J; Yue, C; Zhang, Y-M
A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.
Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?
Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.
Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.
A forward bias method for lag correction of an a-Si flat panel detector
International Nuclear Information System (INIS)
Starman, Jared; Tognina, Carlo; Partain, Larry; Fahrig, Rebecca
Purpose: Digital a-Si flat panel (FP) x-ray detectors can exhibit detector lag, or residual signal, of several percent that can cause ghosting in projection images or severe shading artifacts, known as the radar artifact, in cone-beam computed tomography (CBCT) reconstructions. A major contributor to detector lag is believed to be defect states, or traps, in the a-Si layer of the FP. Software methods to characterize and correct for the detector lag exist, but they may make assumptions such as system linearity and time invariance, which may not be true. The purpose of this work is to investigate a new hardware based method to reduce lag in an a-Si FP and to evaluate its effectiveness at removing shading artifacts in CBCT reconstructions. The feasibility of a novel, partially hardware based solution is also examined. Methods: The proposed hardware solution for lag reduction requires only a minor change to the FP. For pulsed irradiation, the proposed method inserts a new operation step between the readout and data collection stages. During this new stage the photodiode is operated in a forward bias mode, which fills the defect states with charge. A Varian 4030CB panel was modified to allow for operation in the forward bias mode. The contrast of residual lag ghosts was measured for lag frames 2 and 100 after irradiation ceased for standard and forward bias modes. Detector step response, lag, SNR, modulation transfer function (MTF), and detective quantum efficiency (DQE) measurements were made with standard and forward bias firmware. CBCT data of pelvic and head phantoms were also collected. Results: Overall, the 2nd and 100th detector lag frame residual signals were reduced 70%-88% using the new method. SNR, MTF, and DQE measurements show a small decrease in collected signal and a small increase in noise. The forward bias hardware successfully reduced the radar artifact in the CBCT reconstruction of the pelvic and head phantoms by 48%-81%. Conclusions: Overall, the
An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment
Smitha, P. S.; Narasimhan, B.; Sudheer, K. P.; Annamalai, H.
Regional climate models (RCMs) are used to downscale the coarse resolution General Circulation Model (GCM) outputs to a finer resolution for hydrological impact studies. However, RCM outputs often deviate from the observed climatological data, and therefore need bias correction before they are used for hydrological simulations. While there are a number of methods for bias correction, most of them use monthly statistics to derive correction factors, which may cause errors in the rainfall magnitude when applied on a daily scale. This study proposes a sliding window based daily correction factor derivations that help build reliable daily rainfall data from climate models. The procedure is applied to five existing bias correction methods, and is tested on six watersheds in different climatic zones of India for assessing the effectiveness of the corrected rainfall and the consequent hydrological simulations. The bias correction was performed on rainfall data downscaled using Conformal Cubic Atmospheric Model (CCAM) to 0.5° × 0.5° from two different CMIP5 models (CNRM-CM5.0, GFDL-CM3.0). The India Meteorological Department (IMD) gridded (0.25° × 0.25°) observed rainfall data was considered to test the effectiveness of the proposed bias correction method. The quantile-quantile (Q-Q) plots and Nash Sutcliffe efficiency (NSE) were employed for evaluation of different methods of bias correction. The analysis suggested that the proposed method effectively corrects the daily bias in rainfall as compared to using monthly factors. The methods such as local intensity scaling, modified power transformation and distribution mapping, which adjusted the wet day frequencies, performed superior compared to the other methods, which did not consider adjustment of wet day frequencies. The distribution mapping method with daily correction factors was able to replicate the daily rainfall pattern of observed data with NSE value above 0.81 over most parts of India. Hydrological
An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations
Xu, Zhongfeng; Yang, Zong-Liang
An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model
A Realization of Bias Correction Method in the GMAO Coupled System
Chang, Yehui; Koster, Randal; Wang, Hailan; Schubert, Siegfried; Suarez, Max
Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.
Approximate Bias Correction in Econometrics
James G. MacKinnon; Anthony A. Smith Jr.
This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mea...
Bias-correction in vector autoregressive models
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...
Bias Correction Methods Explain Much of the Variation Seen in Breast Cancer Risks of BRCA1/2 Mutation Carriers.
Vos, Janet R; Hsu, Li; Brohet, Richard M; Mourits, Marian J E; de Vries, Jakob; Malone, Kathleen E; Oosterwijk, Jan C; de Bock, Geertruida H
Recommendations for treating patients who carry a BRCA1/2 gene are mainly based on cumulative lifetime risks (CLTRs) of breast cancer determined from retrospective cohorts. These risks vary widely (27% to 88%), and it is important to understand why. We analyzed the effects of methods of risk estimation and bias correction and of population factors on CLTRs in this retrospective clinical cohort of BRCA1/2 carriers. The following methods to estimate the breast cancer risk of BRCA1/2 carriers were identified from the literature: Kaplan-Meier, frailty, and modified segregation analyses with bias correction consisting of including or excluding index patients combined with including or excluding first-degree relatives (FDRs) or different conditional likelihoods. These were applied to clinical data of BRCA1/2 families derived from our family cancer clinic for whom a simulation was also performed to evaluate the methods. CLTRs and 95% CIs were estimated and compared with the reference CLTRs. CLTRs ranged from 35% to 83% for BRCA1 and 41% to 86% for BRCA2 carriers at age 70 years width of 95% CIs: 10% to 35% and 13% to 46%, respectively). Relative bias varied from -38% to +16%. Bias correction with inclusion of index patients and untested FDRs gave the smallest bias: +2% (SD, 2%) in BRCA1 and +0.9% (SD, 3.6%) in BRCA2. Much of the variation in breast cancer CLTRs in retrospective clinical BRCA1/2 cohorts is due to the bias-correction method, whereas a smaller part is due to population differences. Kaplan-Meier analyses with bias correction that includes index patients and a proportion of untested FDRs provide suitable CLTRs for carriers counseled in the clinic. © 2015 by American Society of Clinical Oncology.
Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples
DEFF Research Database (Denmark)
Thorson, James T.; Kristensen, Kasper
Statistical models play an important role in fisheries science when reconciling ecological theory with available data for wild populations or experimental studies. Ecological models increasingly include both fixed and random effects, and are often estimated using maximum likelihood techniques...... configurations of an age-structured population dynamics model. This simulation experiment shows that the epsilon-method and the existing bias-correction method perform equally well in data-rich contexts, but the epsilon-method is slightly less biased in data-poor contexts. We then apply the epsilon......-method to a spatial regression model when estimating an index of population abundance, and compare results with an alternative bias-correction algorithm that involves Markov-chain Monte Carlo sampling. This example shows that the epsilon-method leads to a biologically significant difference in estimates of average...
A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.
Lin, Muqing; Chan, Siwa; Chen, Jeon-Hor; Chang, Daniel; Nie, Ke; Chen, Shih-Ting; Lin, Cheng-Ju; Shih, Tzu-Ching; Nalcioglu, Orhan; Su, Min-Ying
Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3
Bias correction method for climate change impact assessment at a basin scale
Nyunt, C.; Jaranilla-sanchez, P. A.; Yamamoto, A.; Nemoto, T.; Kitsuregawa, M.; Koike, T.
Climate change impact studies are mainly based on the general circulation models GCM and these studies play an important role to define suitable adaptation strategies for resilient environment in a basin scale management. For this purpose, this study summarized how to select appropriate GCM to decrease the certain uncertainty amount in analysis. This was applied to the Pampanga, Angat and Kaliwa rivers in Luzon Island, the main island of Philippine and these three river basins play important roles in irrigation water supply, municipal water source for Metro Manila. According to the GCM scores of both seasonal evolution of Asia summer monsoon and spatial correlation and root mean squared error of atmospheric variables over the region, finally six GCM is chosen. Next, we develop a complete, efficient and comprehensive statistical bias correction scheme covering extremes events, normal rainfall and frequency of dry period. Due to the coarse resolution and parameterization scheme of GCM, extreme rainfall underestimation, too many rain days with low intensity and poor representation of local seasonality have been known as bias of GCM. Extreme rainfall has unusual characteristics and it should be focused specifically. Estimated maximum extreme rainfall is crucial for planning and design of infrastructures in river basin. Developing countries have limited technical, financial and management resources for implementing adaptation measures and they need detailed information of drought and flood for near future. Traditionally, the analysis of extreme has been examined using annual maximum series (AMS) adjusted to a Gumbel or Lognormal distribution. The drawback is the loss of the second, third etc, largest rainfall. Another approach is partial duration series (PDS) constructed using the values above a selected threshold and permit more than one event per year. The generalized Pareto distribution (GPD) has been used to model PDS and it is the series of excess over a threshold
A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation
Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.
Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.
RCP: a novel probe design bias correction method for Illumina Methylation BeadChip.
Niu, Liang; Xu, Zongli; Taylor, Jack A
The Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis. Here, we developed a novel method, called Regression on Correlated Probes (RCP), which uses the existing correlation between pairs of nearby type I and II probes to adjust the beta values of all type II probes. We evaluate the effect of this adjustment on reducing probe design type bias, reducing technical variation in duplicate samples, improving accuracy of measurements against known standards, and retention of biological signal. We find that RCP is statistically significantly better than unadjusted data or adjustment with alternative methods including SWAN and BMIQ. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html). email@example.com Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.
An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations
Xu, Zhongfeng
An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research\\'s (NCAR\\'s) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)-NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.-Canada region as well as in summer and winter daily precipitation over the middle and eastern United States. © 2012 American Meteorological Society.
A method for additive bias correction in cross-cultural surveys
DEFF Research Database (Denmark)
Scholderer, Joachim; Grunert, Klaus G.; Brunsø, Karen
additive bias from cross-cultural data. The procedure involves four steps: (1) embed a potentially biased item in a factor-analytic measurement model, (2) test for the existence of additive bias between populations, (3) use the factor-analytic model to estimate the magnitude of the bias, and (4) replace......Measurement bias in cross-cultural surveys can seriously threaten the validity of hypothesis tests. Direct comparisons of means depend on the assumption that differences in observed variables reflect differences in the underlying constructs, and not an additive bias that may be caused by cultural...... differences in the understanding of item wording or response category labels. However, experience suggests that additive bias can be found more often than not. Based on the concept of partial measurement invariance (Byrne, Shavelson and Muthén, 1989), the present paper develops a procedure for eliminating...
Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.
Batistatou, Evridiki; McNamee, Roseanne
It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.
Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study
Directory of Open Access Journals (Sweden)
Vickers Andrew J
Full Text Available Abstract Background A common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives. Methods A simulation study was conducted of a screening study subject to verification bias. We compared estimates of the area-under-the-curve (AUC corrected for verification bias varying both the rate and mechanism of verification. Results In a single simulated data set, varying false negatives from 0 to 4 led to verification bias corrected AUCs ranging from 0.550 to 0.852. Excess variation associated with low numbers of false negatives was confirmed in simulation studies and by analyses of published studies that incorporated verification bias correction. The 2.5th – 97.5th centile range constituted as much as 60% of the possible range of AUCs for some simulations. Conclusion Screening programs are designed such that there are few false negatives. Standard statistical methods for verification bias correction are inadequate in this circumstance.
Estimation bias and bias correction in reduced rank autoregressions
DEFF Research Database (Denmark)
Nielsen, Heino Bohn
This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...
Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location
DEFF Research Database (Denmark)
Fennema-Notestine, Christine; Ozyurt, I Burak; Clark, Camellia P
Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed...... distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more...
Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations.
Lubow, Bruce C; Ransom, Jason I
Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.
Bias-Correction in Vector Autoregressive Models: A Simulation Study
Directory of Open Access Journals (Sweden)
Tom Engsted
Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
A bias-corrected CMIP5 dataset for Africa using the CDF-t method - a contribution to agricultural impact studies
Moise Famien, Adjoua; Janicot, Serge; Delfin Ochou, Abe; Vrac, Mathieu; Defrance, Dimitri; Sultan, Benjamin; Noël, Thomas
The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950-2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.
Bias Correction with Jackknife, Bootstrap, and Taylor Series
Jiao, Jiantao; Han, Yanjun; Weissman, Tsachy
We analyze the bias correction methods using jackknife, bootstrap, and Taylor series. We focus on the binomial model, and consider the problem of bias correction for estimating $f(p)$, where $f \\in C[0,1]$ is arbitrary. We characterize the supremum norm of the bias of general jackknife and bootstrap estimators for any continuous functions, and demonstrate the in delete-$d$ jackknife, different values of $d$ may lead to drastically different behavior in jackknife. We show that in the binomial ...
Bias-correction in vector autoregressive models: A simulation study
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....
On the Limitations of Variational Bias Correction
Moradi, Isaac; Mccarty, Will; Gelaro, Ronald
Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.
Improved Correction of Misclassification Bias With Bootstrap Imputation.
van Walraven, Carl
Diagnostic codes used in administrative database research can create bias due to misclassification. Quantitative bias analysis (QBA) can correct for this bias, requires only code sensitivity and specificity, but may return invalid results. Bootstrap imputation (BI) can also address misclassification bias but traditionally requires multivariate models to accurately estimate disease probability. This study compared misclassification bias correction using QBA and BI. Serum creatinine measures were used to determine severe renal failure status in 100,000 hospitalized patients. Prevalence of severe renal failure in 86 patient strata and its association with 43 covariates was determined and compared with results in which renal failure status was determined using diagnostic codes (sensitivity 71.3%, specificity 96.2%). Differences in results (misclassification bias) were then corrected with QBA or BI (using progressively more complex methods to estimate disease probability). In total, 7.4% of patients had severe renal failure. Imputing disease status with diagnostic codes exaggerated prevalence estimates [median relative change (range), 16.6% (0.8%-74.5%)] and its association with covariates [median (range) exponentiated absolute parameter estimate difference, 1.16 (1.01-2.04)]. QBA produced invalid results 9.3% of the time and increased bias in estimates of both disease prevalence and covariate associations. BI decreased misclassification bias with increasingly accurate disease probability estimates. QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.
Bias correction for magnetic resonance images via joint entropy regularization.
Wang, Shanshan; Xia, Yong; Dong, Pei; Luo, Jianhua; Huang, Qiu; Feng, Dagan; Li, Yuanxiang
Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.
Determination and Correction of Persistent Biases in Quantum Annealers
for all of the qubits. Narrowing of the bias distribution. To show the correctability of the persistent biases , we ran the experiment described above...this is a promising application for bias correction . Importantly, while the J biases determined here are in general smaller than the h biases , numerical...1Scientific RepoRts | 6:18628 | DOI: 10.1038/srep18628 www.nature.com/scientificreports Determination and correction of persistent biases in quantum
Efficient bias correction for magnetic resonance image denoising.
Mukherjee, Partha Sarathi; Qiu, Peihua
Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications. Copyright © 2012 John Wiley & Sons, Ltd.
Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location
DEFF Research Database (Denmark)
Fennema-Notestine, Christine; Ozyurt, I Burak; Clark, Camellia P
Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four...... methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060-1075; in FreeSurfer); and Brain Surface...... Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed...
Incorporating circulation statistics in bias correction of GCM ensembles: Hydrological application for the Rhine basin
Photiadou, C.; van den Hurk, B.J.J.M.; Delden, A. van; Weerts, A.
An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to
Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin
Photiadou, C.; Hurk, van den B.; Delden, van A.; Weerts, A.H.
An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to
Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin
Photiadou, Christiana; van den Hurk, Bart; van Delden, Aarnout; Weerts, Albrecht
An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a histori- cal period. The bias correction method is tailored to time scales relevant to
Towards improving the reliability of future regional climate projections: A bias-correction method applied to precipitation over the west coast of Norway
Valved, A.; Barstad, I.; Sobolowski, S.
The early winter of 2011/2012 in the city of Bergen, located on the west coast of Norway, was dominated by warm, wet and extreme weather. This might be a glimpse of future average climate conditions under continued atmospheric warming and an enhanced hydrological cycle. The extreme weather events have resulted in drainage/sewage problems, landslides, flooding property damage and even death. As the Municipality plans for the future they must contend with a growing population in a geographically complex area in addition to any effects attributable to climate change. While the scientific community is increasingly confident in the projections of large scale changes over the mid - high latitudes this confidence does not extend to the local - regional scale where the magnitude and even direction of change may be highly uncertain. Meanwhile it is precisely these scales that Municipalities such as Bergen require information if they are to plan effectively. Thus, there is a need for reliable, local climate projections, which can aid policy makers and planners in decision-making. Current state of the art regional climate models are capable of providing detailed simulations on the order of 1 or 10km. However, due to the increased computational demands of these simulations, large ensembles, such as those used for GCM experiments, are often not possible. Thus, greater detail, under these circumstances, does not necessarily correspond to greater reliability. One way to deal with this issue is to apply a statistical bias correction method where model results are fitted to observationally derived probability density functions (pdfs). In this way, a full distribution of potential changes may be generated which are constrained by known, observed data.This will result in a shifted model distribution with mean and spread that more closely follows observations. In short, the method temporarily removes the climate signals from the model run working on the different percentiles, fits the
Noise Induces Biased Estimation of the Correction Gain.
Directory of Open Access Journals (Sweden)
Jooeun Ahn
Full Text Available The detection of an error in the motor output and the correction in the next movement are critical components of any form of motor learning. Accordingly, a variety of iterative learning models have assumed that a fraction of the error is adjusted in the next trial. This critical fraction, the correction gain, learning rate, or feedback gain, has been frequently estimated via least-square regression of the obtained data set. Such data contain not only the inevitable noise from motor execution, but also noise from measurement. It is generally assumed that this noise averages out with large data sets and does not affect the parameter estimation. This study demonstrates that this is not the case and that in the presence of noise the conventional estimate of the correction gain has a significant bias, even with the simplest model. Furthermore, this bias does not decrease with increasing length of the data set. This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation. We derive an analytical form of the bias from a simple regression method (Yule-Walker and develop an improved identification method. This bias is discussed as one of other examples for how the dynamics of noise can introduce significant distortions in data analysis.
Moisture Forecast Bias Correction in GEOS DAS
Dee, D.
Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.
Correction for the Hematocrit Bias in Dried Blood Spot Analysis Using a Nondestructive, Single-Wavelength Reflectance-Based Hematocrit Prediction Method.
Capiau, Sara; Wilk, Leah S; De Kesel, Pieter M M; Aalders, Maurice C G; Stove, Christophe P
bias obtained with Bland and Altman analysis was -0.015 and the limits of agreement were -0.061 and 0.031, indicating that the simplified, noncontact Hct prediction method even outperforms the original method. In addition, using caffeine as a model compound, it was demonstrated that this simplified Hct prediction method can effectively be used to implement a Hct-dependent correction factor to DBS-based results to alleviate the Hct bias.
Application of bias correction methods to improve U3Si2 sample preparation for quantitative analysis by WDXRF
International Nuclear Information System (INIS)
Scapin, Marcos A.; Guilhen, Sabine N.; Azevedo, Luciana C. de; Cotrim, Marycel E.B.; Pires, Maria Ap. F.
The determination of silicon (Si), total uranium (U) and impurities in uranium-silicide (U 3 Si 2 ) samples by wavelength dispersion X-ray fluorescence technique (WDXRF) has been already validated and is currently implemented at IPEN's X-Ray Fluorescence Laboratory (IPEN-CNEN/SP) in São Paulo, Brazil. Sample preparation requires the use of approximately 3 g of H 3 BO 3 as sample holder and 1.8 g of U 3 Si 2 . However, because boron is a neutron absorber, this procedure precludes U 3 Si 2 sample's recovery, which, in time, considering routinely analysis, may account for significant unusable uranium waste. An estimated average of 15 samples per month are expected to be analyzed by WDXRF, resulting in approx. 320 g of U 3 Si 2 that would not return to the nuclear fuel cycle. This not only impacts in production losses, but generates another problem: radioactive waste management. The purpose of this paper is to present the mathematical models that may be applied for the correction of systematic errors when H 3 BO 3 sample holder is substituted by cellulose-acetate {[C 6 H 7 O 2 (OH) 3-m (OOCCH 3 )m], m = 0∼3}, thus enabling U 3 Si 2 sample's recovery. The results demonstrate that the adopted mathematical model is statistically satisfactory, allowing the optimization of the procedure. (author)
Reduction of density-modification bias by β correction
International Nuclear Information System (INIS)
Skubák, Pavol; Pannu, Navraj S.
A cross-validation-based method for bias reduction in 'classical' iterative density modification of experimental X-ray crystallography maps provides significantly more accurate phase-quality estimates and leads to improved automated model building. Density modification often suffers from an overestimation of phase quality, as seen by escalated figures of merit. A new cross-validation-based method to address this estimation bias by applying a bias-correction parameter 'β' to maximum-likelihood phase-combination functions is proposed. In tests on over 100 single-wavelength anomalous diffraction data sets, the method is shown to produce much more reliable figures of merit and improved electron-density maps. Furthermore, significantly better results are obtained in automated model building iterated with phased refinement using the more accurate phase probability parameters from density modification
Bias-corrected estimation in potentially mildly explosive autoregressive models
DEFF Research Database (Denmark)
Haufmann, Hendrik; Kruse, Robinson
This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...
On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology
Energy Technology Data Exchange (ETDEWEB)
Valdes Hernandez, Maria del C.; Gonzalez-Castro, Victor; Wang, Xin; Doubal, Fergus; Munoz Maniega, Susana; Wardlaw, Joanna M. [Centre for Clinical Brian Sciences, Department of Neuroimaging Sciences, Edinburgh (United Kingdom); Ghandour, Dina T. [University of Edinburgh, College of Medicine and Veterinary Medicine, Edinburgh (United Kingdom); Armitage, Paul A. [University of Sheffield, Department of Cardiovascular Sciences, Sheffield (United Kingdom)
Subtle inhomogeneities in the scanner's magnetic fields (B{sub 0} and B{sub 1}) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B{sub 1} inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions. We used brain structural MRI from 46 mild stroke patients scanned at stroke onset and 3 years later. We tested three BFC approaches: FSL-FAST, N4 and exponentially entropy-driven homomorphic unsharp masking (E{sup 2}D-HUM) and analysed their effect on the measured WMH change. Separately, we tested two methods to assess WMH changes: measuring WMH volumes independently at both time points semi-automatically (MCMxxxVI) and subtracting intensity-normalised FLAIR images at both time points following image gamma correction. We then combined the BFC with the computational method that performed best across the whole sample to assess WMH changes. Analysis of the difference in the variance-to-mean intensity ratio in normal tissue between BFC and uncorrected images and visual inspection showed that all BFC methods altered the WMH appearance and distribution, but FSL-FAST in general performed more consistently across the sample and MRI modalities. The WMH volume change over 3 years obtained with MCMxxxVI with vs. without FSL-FAST BFC did not significantly differ (medians(IQR)(with BFC) = 3.2(6.3) vs. 2.9(7.4)ml (without BFC), p = 0.5), but both differed significantly from the WMH volume change obtained from subtracting post-processed FLAIR images (without BFC)(7.6(8.2)ml, p < 0.001). This latter method considerably inflated the WMH volume change as subtle WMH at baseline that became more intense at follow-up were counted as increase in the volumetric change. Measurement of WMH volume change remains
Bias Correction Methods Explain Much of the Variation Seen in Breast Cancer Risks of BRCA1/2 Mutation Carriers
Vos, Janet R.; Hsu, Li; Brohet, Richard M.; Mourits, Marian J. E.; de Vries, Jakob; Malone, Kathleen E.; Oosterwijk, Jan C.; de Bock, Geertruida H.
Purpose Recommendations for treating patients who carry a BRCA1/2 gene are mainly based on cumulative lifetime risks (CLTRs) of breast cancer determined from retrospective cohorts. These risks vary widely (27% to 88%), and it is important to understand why. We analyzed the effects of methods of risk
Correction the Bias of Odds Ratio resulting from the Misclassification of Exposures in the Study of Environmental Risk Factors of Lung Cancer using Bayesian Methods
Directory of Open Access Journals (Sweden)
Alireza Abadi
Full Text Available Background & Objective: Inability to measure exact exposure in epidemiological studies is a common problem in many studies, especially cross-sectional studies. Depending on the extent of misclassification, results may be affected. Existing methods for solving this problem require a lot of time and money and it is not practical for some of the exposures. Recently, new methods have been proposed in 1:1 matched case–control studies that have solved these problems to some extent. In the present study we have aimed to extend the existing Bayesian method to adjust for misclassification in matched case–control Studies with 1:2 matching. Methods: Here, the standard Dirichlet prior distribution for a multinomial model was extended to allow the data of exposure–disease (OR parameter to be imported into the model excluding other parameters. Information that exist in literature about association between exposure and disease were used as prior information about OR. In order to correct the misclassification Sensitivity Analysis was accomplished and the results were obtained under three Bayesian Methods. Results: The results of naïve Bayesian model were similar to the classic model. The second Bayesian model by employing prior information about the OR, was heavily affected by these information. The third proposed model provides maximum bias adjustment for the risk of heavy metals, smoking and drug abuse. This model showed that heavy metals are not an important risk factor although raw model (logistic regression Classic detected this exposure as an influencing factor on the incidence of lung cancer. Sensitivity analysis showed that third model is robust regarding to different levels of Sensitivity and Specificity. Conclusion: The present study showed that although in most of exposures the results of the second and third model were similar but the proposed model would be able to correct the misclassification to some extent.
Assessing atmospheric bias correction for dynamical consistency using potential vorticity
International Nuclear Information System (INIS)
Rocheta, Eytan; Sharma, Ashish; Evans, Jason P
Correcting biases in atmospheric variables prior to impact studies or dynamical downscaling can lead to new biases as dynamical consistency between the 'corrected' fields is not maintained. Use of these bias corrected fields for subsequent impact studies and dynamical downscaling provides input conditions that do not appropriately represent intervariable relationships in atmospheric fields. Here we investigate the consequences of the lack of dynamical consistency in bias correction using a measure of model consistency—the potential vorticity (PV). This paper presents an assessment of the biases present in PV using two alternative correction techniques—an approach where bias correction is performed individually on each atmospheric variable, thereby ignoring the physical relationships that exists between the multiple variables that are corrected, and a second approach where bias correction is performed directly on the PV field, thereby keeping the system dynamically coherent throughout the correction process. In this paper we show that bias correcting variables independently results in increased errors above the tropopause in the mean and standard deviation of the PV field, which are improved when using the alternative proposed. Furthermore, patterns of spatial variability are improved over nearly all vertical levels when applying the alternative approach. Results point to a need for a dynamically consistent atmospheric bias correction technique which results in fields that can be used as dynamically consistent lateral boundaries in follow-up downscaling applications. (letter)
Correction of bias in belt transect studies of immotile objects
Anderson, D.R.; Pospahala, R.S.
Unless a correction is made, population estimates derived from a sample of belt transects will be biased if a fraction of, the individuals on the sample transects are not counted. An approach, useful for correcting this bias when sampling immotile populations using transects of a fixed width, is presented. The method assumes that a searcher's ability to find objects near the center of the transect is nearly perfect. The method utilizes a mathematical equation, estimated from the data, to represent the searcher's inability to find all objects at increasing distances from the center of the transect. An example of the analysis of data, formation of the equation, and application is presented using waterfowl nesting data collected in Colorado.
Estimation of satellite position, clock and phase bias corrections
Henkel, Patrick; Psychas, Dimitrios; Günther, Christoph; Hugentobler, Urs
Precise point positioning with integer ambiguity resolution requires precise knowledge of satellite position, clock and phase bias corrections. In this paper, a method for the estimation of these parameters with a global network of reference stations is presented. The method processes uncombined and undifferenced measurements of an arbitrary number of frequencies such that the obtained satellite position, clock and bias corrections can be used for any type of differenced and/or combined measurements. We perform a clustering of reference stations. The clustering enables a common satellite visibility within each cluster and an efficient fixing of the double difference ambiguities within each cluster. Additionally, the double difference ambiguities between the reference stations of different clusters are fixed. We use an integer decorrelation for ambiguity fixing in dense global networks. The performance of the proposed method is analysed with both simulated Galileo measurements on E1 and E5a and real GPS measurements of the IGS network. We defined 16 clusters and obtained satellite position, clock and phase bias corrections with a precision of better than 2 cm.
Implementation of linear bias corrections for calorimeters at Mound
International Nuclear Information System (INIS)
Barnett, T.M.
In the past, Mound has generally made relative bias corrections as part of the calibration of individual calorimeters. The correction made was the same over the entire operating range of the calorimeter, regardless of the magnitude of the range. Recently, an investigation was performed to check the relevancy of using linear bias corrections to calibrate the calorimeters. The bias is obtained by measuring calibrated plutonium and/or electrical heat standards over the operating range of the calorimeter. The bias correction is then calculated using a simple least squares fit (y = mx + b) of the bias in milliwatts over the operating range of the calorimeter in watts. The equation used is B i = B 0 + (B w * W m ), where B i is the bias at any given power in milliwatts, B 0 is the intercept (absolute bias in milliwatts), B w is the slope (relative bias in milliwatts per watt), and W m is the measured power in watts. The results of the study showed a decrease in the random error of bias corrected data for most of the calorimeters which are operated over a large wattage range (greater than an order of magnitude). The linear technique for bias correction has been fully implemented at Mound and has been included in the Technical Manual, ''A Measurement Control Program for Radiometric Calorimeters at Mound'' (MD-21900)
A Variational Approach to Simultaneous Image Segmentation and Bias Correction.
Zhang, Kaihua; Liu, Qingshan; Song, Huihui; Li, Xuelong
This paper presents a novel variational approach for simultaneous estimation of bias field and segmentation of images with intensity inhomogeneity. We model intensity of inhomogeneous objects to be Gaussian distributed with different means and variances, and then introduce a sliding window to map the original image intensity onto another domain, where the intensity distribution of each object is still Gaussian but can be better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying the bias field with a piecewise constant signal within the sliding window. A maximum likelihood energy functional is then defined on each local region, which combines the bias field, the membership function of the object region, and the constant approximating the true signal from its corresponding object. The energy functional is then extended to the whole image domain by the Bayesian learning approach. An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved. Furthermore, the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost. Experiments on real images demonstrated the superiority of the proposed algorithm to other state-of-the-art representative methods.
Correcting for particle counting bias error in turbulent flow
Edwards, R. V.; Baratuci, W.
An ideal seeding device is proposed generating particles that exactly follow the flow out are still a major source of error, i.e., with a particle counting bias wherein the probability of measuring velocity is a function of velocity. The error in the measured mean can be as much as 25%. Many schemes have been put forward to correct for this error, but there is not universal agreement as to the acceptability of any one method. In particular it is sometimes difficult to know if the assumptions required in the analysis are fulfilled by any particular flow measurement system. To check various correction mechanisms in an ideal way and to gain some insight into how to correct with the fewest initial assumptions, a computer simulation is constructed to simulate laser anemometer measurements in a turbulent flow. That simulator and the results of its use are discussed.
Bias-corrected Pearson estimating functions for Taylor's power law applied to benthic macrofauna data
DEFF Research Database (Denmark)
Jørgensen, Bent; Demétrio, Clarice G. B.; Kristensen, Erik
Estimation of Taylor's power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating...
Bias correction of daily satellite precipitation data using genetic algorithm
Pratama, A. W.; Buono, A.; Hidayat, R.; Harsa, H.
Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was producted by blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.
An introduction to Bartlett correction and bias reduction
Cordeiro, Gauss M
This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique, and discuss concrete applications to several important statistical models.
Model Correction Factor Method
DEFF Research Database (Denmark)
Christensen, Claus; Randrup-Thomsen, Søren; Morsing Johannesen, Johannes
The model correction factor method is proposed as an alternative to traditional polynomial based response surface techniques in structural reliability considering a computationally time consuming limit state procedure as a 'black box'. The class of polynomial functions is replaced by a limit...... of the model correction factor method, is that in simpler form not using gradient information on the original limit state function or only using this information once, a drastic reduction of the number of limit state evaluation is obtained together with good approximations on the reliability. Methods...
Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts
DEFF Research Database (Denmark)
Seaby, Lauren Paige; Refsgaard, Jens Christian; Sonnenborg, Torben
Model pairing, this paper analyses the relationship between complexity and robustness of three distribution-based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty......The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate...... signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological...
Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data
International Nuclear Information System (INIS)
Zhang, L.F.; Xie, M.; Tang, L.C.
Estimation of the Weibull shape parameter is important in reliability engineering. However, commonly used methods such as the maximum likelihood estimation (MLE) and the least squares estimation (LSE) are known to be biased. Bias correction methods for MLE have been studied in the literature. This paper investigates the methods for bias correction when model parameters are estimated with LSE based on probability plot. Weibull probability plot is very simple and commonly used by practitioners and hence such a study is useful. The bias of the LS shape parameter estimator for multiple censored data is also examined. It is found that the bias can be modeled as the function of the sample size and the censoring level, and is mainly dependent on the latter. A simple bias function is introduced and bias correcting formulas are proposed for both complete and censored data. Simulation results are also presented. The bias correction methods proposed are very easy to use and they can typically reduce the bias of the LSE of the shape parameter to less than half percent
Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali island
Lealdi, D.; Nurdiati, S.; Sopaheluwakan, A.
Rainfall is an element of climate which is highly influential to the agricultural sector. Rain pattern and distribution highly determines the sustainability of agricultural activities. Therefore, information on rainfall is very useful for agriculture sector and farmers in anticipating the possibility of extreme events which often cause failures of agricultural production. This research aims to identify the biases from seasonal forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) rainfall forecast and to build a transfer function in order to correct the distribution biases as a new prediction model using quantile mapping approach. We apply this approach to the case of Bali Island, and as a result, the use of bias correction methods in correcting systematic biases from the model gives better results. The new prediction model obtained with this approach is better than ever. We found generally that during rainy season, the bias correction approach performs better than in dry season.
Estimation and correction of visibility bias in aerial surveys of wintering ducks
Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.
Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.
Measured attenuation correction methods
International Nuclear Information System (INIS)
Ostertag, H.; Kuebler, W.K.; Doll, J.; Lorenz, W.J.
Accurate attenuation correction is a prerequisite for the determination of exact local radioactivity concentrations in positron emission tomography. Attenuation correction factors range from 4-5 in brain studies to 50-100 in whole body measurements. This report gives an overview of the different methods of determining the attenuation correction factors by transmission measurements using an external positron emitting source. The long-lived generator nuclide 68 Ge/ 68 Ga is commonly used for this purpose. The additional patient dose from the transmission source is usually a small fraction of the dose due to the subsequent emission measurement. Ring-shaped transmission sources as well as rotating point or line sources are employed in modern positron tomographs. By masking a rotating line or point source, random and scattered events in the transmission scans can be effectively suppressed. The problems of measured attenuation correction are discussed: Transmission/emission mismatch, random and scattered event contamination, counting statistics, transmission/emission scatter compensation, transmission scan after administration of activity to the patient. By using a double masking technique simultaneous emission and transmission scans become feasible. (orig.)
Averaging Bias Correction for Future IPDA Lidar Mission MERLIN
Directory of Open Access Journals (Sweden)
Tellier Yoann
Full Text Available The CNES/DLR MERLIN satellite mission aims at measuring methane dry-air mixing ratio column (XCH4 and thus improving surface flux estimates. In order to get a 1% precision on XCH4 measurements, MERLIN signal processing assumes an averaging of data over 50 km. The induced biases due to the non-linear IPDA lidar equation are not compliant with accuracy requirements. This paper analyzes averaging biases issues and suggests correction algorithms tested on realistic simulated scenes.
Averaging Bias Correction for Future IPDA Lidar Mission MERLIN
Tellier, Yoann; Pierangelo, Clémence; Wirth, Martin; Gibert, Fabien
The CNES/DLR MERLIN satellite mission aims at measuring methane dry-air mixing ratio column (XCH4) and thus improving surface flux estimates. In order to get a 1% precision on XCH4 measurements, MERLIN signal processing assumes an averaging of data over 50 km. The induced biases due to the non-linear IPDA lidar equation are not compliant with accuracy requirements. This paper analyzes averaging biases issues and suggests correction algorithms tested on realistic simulated scenes.
Experimenter Confirmation Bias and the Correction of Science Misconceptions
Allen, Michael; Coole, Hilary
This paper describes a randomised educational experiment ( n = 47) that examined two different teaching methods and compared their effectiveness at correcting one science misconception using a sample of trainee primary school teachers. The treatment was designed to promote engagement with the scientific concept by eliciting emotional responses from learners that were triggered by their own confirmation biases. The treatment group showed superior learning gains to control at post-test immediately after the lesson, although benefits had dissipated after 6 weeks. Findings are discussed with reference to the conceptual change paradigm and to the importance of feeling emotion during a learning experience, having implications for the teaching of pedagogies to adults that have been previously shown to be successful with children.
Assessing the implementation of bias correction in the climate prediction
Nadrah Aqilah Tukimat, Nurul
An issue of the climate changes nowadays becomes trigger and irregular. The increment of the greenhouse gases (GHGs) emission into the atmospheric system day by day gives huge impact to the fluctuated weather and global warming. It becomes significant to analyse the changes of climate parameters in the long term. However, the accuracy in the climate simulation is always be questioned to control the reliability of the projection results. Thus, the Linear Scaling (LS) as a bias correction method (BC) had been applied to treat the gaps between observed and simulated results. About two rainfall stations were selected in Pahang state there are Station Lubuk Paku and Station Temerloh. Statistical Downscaling Model (SDSM) used to perform the relationship between local weather and atmospheric parameters in projecting the long term rainfall trend. The result revealed the LS was successfully to reduce the error up to 3% and produced better climate simulated results.
Silicon photomultiplier's gain stabilization by bias correction for compensation of the temperature fluctuations
Energy Technology Data Exchange (ETDEWEB)
Dorosz, P., E-mail: email@example.com [AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Electronics, 30-059 Krakow (Poland); Baszczyk, M.; Glab, S. [AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Electronics, 30-059 Krakow (Poland); Kucewicz, W., E-mail: firstname.lastname@example.com [AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Electronics, 30-059 Krakow (Poland); Mik, L.; Sapor, M. [AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Electronics, 30-059 Krakow (Poland)
Gain of the silicon photomultiplier is strongly dependent on the value of bias voltage and temperature. This paper proposes a method for gain stabilization just by compensation of temperature fluctuations by bias correction. It has been confirmed that this approach gives good results and the gain can be kept very stable.
Silicon photomultiplier's gain stabilization by bias correction for compensation of the temperature fluctuations
International Nuclear Information System (INIS)
Dorosz, P.; Baszczyk, M.; Glab, S.; Kucewicz, W.; Mik, L.; Sapor, M.
Gain of the silicon photomultiplier is strongly dependent on the value of bias voltage and temperature. This paper proposes a method for gain stabilization just by compensation of temperature fluctuations by bias correction. It has been confirmed that this approach gives good results and the gain can be kept very stable
A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator
Directory of Open Access Journals (Sweden)
Munir Ahmed
Full Text Available In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000 introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003 present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs'. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.
Effect of sample size on bias correction performance
Reiter, Philipp; Gutjahr, Oliver; Schefczyk, Lukas; Heinemann, Günther; Casper, Markus C.
The output of climate models often shows a bias when compared to observed data, so that a preprocessing is necessary before using it as climate forcing in impact modeling (e.g. hydrology, species distribution). A common bias correction method is the quantile matching approach, which adapts the cumulative distribution function of the model output to the one of the observed data by means of a transfer function. Especially for precipitation we expect the bias correction performance to strongly depend on sample size, i.e. the length of the period used for calibration of the transfer function. We carry out experiments using the precipitation output of ten regional climate model (RCM) hindcast runs from the EU-ENSEMBLES project and the E-OBS observational dataset for the period 1961 to 2000. The 40 years are split into a 30 year calibration period and a 10 year validation period. In the first step, for each RCM transfer functions are set up cell-by-cell, using the complete 30 year calibration period. The derived transfer functions are applied to the validation period of the respective RCM precipitation output and the mean absolute errors in reference to the observational dataset are calculated. These values are treated as "best fit" for the respective RCM. In the next step, this procedure is redone using subperiods out of the 30 year calibration period. The lengths of these subperiods are reduced from 29 years down to a minimum of 1 year, only considering subperiods of consecutive years. This leads to an increasing number of repetitions for smaller sample sizes (e.g. 2 for a length of 29 years). In the last step, the mean absolute errors are statistically tested against the "best fit" of the respective RCM to compare the performances. In order to analyze if the intensity of the effect of sample size depends on the chosen correction method, four variations of the quantile matching approach (PTF, QUANT/eQM, gQM, GQM) are applied in this study. The experiments are further
Multisite bias correction of precipitation data from regional climate models
Czech Academy of Sciences Publication Activity Database
Hnilica, Jan; Hanel, M.; Puš, V.
Roč. 37, č. 6 (2017), s. 2934-2946 ISSN 0899-8418 R&D Projects: GA ČR GA16-05665S Grant - others:Grantová agentura ČR - GA ČR(CZ) 16-16549S Institutional support: RVO:67985874 Keywords : bias correction * regional climate model * correlation * covariance * multivariate data * multisite correction * principal components * precipitation Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Climatic research Impact factor: 3.760, year: 2016
Bias-corrected estimation of stable tail dependence function
DEFF Research Database (Denmark)
Beirlant, Jan; Escobar-Bach, Mikael; Goegebeur, Yuri
We consider the estimation of the stable tail dependence function. We propose a bias-corrected estimator and we establish its asymptotic behaviour under suitable assumptions. The finite sample performance of the proposed estimator is evaluated by means of an extensive simulation study where...
Recent advances in precipitation-bias correction and application
Institute of Scientific and Technical Information of China (English)
Significant progresses have been made in recent years in precipitation data analyses at regional to global scales. This paper re-views and synthesizes recent advances in precipitation-bias corrections and applications in many countries and over the cold re-gions. The main objective of this review is to identify and examine gaps in regional and national precipitation-error analyses. This paper also discusses and recommends future research needs and directions. More effort and coordination are necessary in the determinations of precipitation biases on large regions across national borders. It is important to emphasize that bias cor-rections of precipitation measurements affect both water budget and energy balance calculations, particularly over the cold regions.
Correction of stream quality trends for the effects of laboratory measurement bias
Alexander, Richard B.; Smith, Richard A.; Schwarz, Gregory E.
We present a statistical model relating measurements of water quality to associated errors in laboratory methods. Estimation of the model allows us to correct trends in water quality for long-term and short-term variations in laboratory measurement errors. An illustration of the bias correction method for a large national set of stream water quality and quality assurance data shows that reductions in the bias of estimates of water quality trend slopes are achieved at the expense of increases in the variance of these estimates. Slight improvements occur in the precision of estimates of trend in bias by using correlative information on bias and water quality to estimate random variations in measurement bias. The results of this investigation stress the need for reliable, long-term quality assurance data and efficient statistical methods to assess the effects of measurement errors on the detection of water quality trends.
Correction of Spatial Bias in Oligonucleotide Array Data
Directory of Open Access Journals (Sweden)
Philippe Serhal
Full Text Available Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays. A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.
Correction of Spatial Bias in Oligonucleotide Array Data
Lemieux, Sébastien
Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. PMID:23573083
Magnification bias corrections to galaxy-lensing cross-correlations
International Nuclear Information System (INIS)
Ziour, Riad; Hui, Lam
Galaxy-galaxy or galaxy-quasar lensing can provide important information on the mass distribution in the Universe. It consists of correlating the lensing signal (either shear or magnification) of a background galaxy/quasar sample with the number density of a foreground galaxy sample. However, the foreground galaxy density is inevitably altered by the magnification bias due to the mass between the foreground and the observer, leading to a correction to the observed galaxy-lensing signal. The aim of this paper is to quantify this correction. The single most important determining factor is the foreground redshift z f : the correction is small if the foreground galaxies are at low redshifts but can become non-negligible for sufficiently high redshifts. For instance, we find that for the multipole l=1000, the correction is above 1%x(5s f -2)/b f for z f > or approx. 0.37, and above 5%x(5s f -2)/b f for z f > or approx. 0.67, where s f is the number count slope of the foreground sample and b f its galaxy bias. These considerations are particularly important for geometrical measures, such as the Jain and Taylor ratio or its generalization by Zhang et al. Assuming (5s f -2)/b f =1, we find that the foreground redshift should be limited to z f < or approx. 0.45 in order to avoid biasing the inferred dark energy equation of state w by more than 5%, and that even for a low foreground redshift (<0.45), the background samples must be well separated from the foreground to avoid incurring a bias of similar magnitude. Lastly, we briefly comment on the possibility of obtaining these geometrical measures without using galaxy shapes, using instead magnification bias itself.
Bias correction for the estimation of sensitivity indices based on random balance designs
International Nuclear Information System (INIS)
Tissot, Jean-Yves; Prieur, Clémentine
This paper deals with the random balance design method (RBD) and its hybrid approach, RBD-FAST. Both these global sensitivity analysis methods originate from Fourier amplitude sensitivity test (FAST) and consequently face the main problems inherent to discrete harmonic analysis. We present here a general way to correct a bias which occurs when estimating sensitivity indices (SIs) of any order – except total SI of single factor or group of factors – by the random balance design method (RBD) and its hybrid version, RBD-FAST. In the RBD case, this positive bias has been recently identified in a paper by Xu and Gertner [1]. Following their work, we propose a bias correction method for first-order SIs estimates in RBD. We then extend the correction method to the SIs of any order in RBD-FAST. At last, we suggest an efficient strategy to estimate all the first- and second-order SIs using RBD-FAST. - Highlights: ► We provide a bias correction method for the global sensitivity analysis methods: RBD and RBD-FAST. ► In RBD, first-order sensitivity estimates are corrected. ► In RBD-FAST, sensitivity indices of any order and closed sensitivity indices are corrected. ► We propose an efficient strategy to estimate all the first- and second-order indices of a model.
Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models
Directory of Open Access Journals (Sweden)
Peter Berg
Full Text Available Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM problematic. We present a method to construct pseudo-observational precipitation data bymerging a large scale constrained RCMreanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models.
Bias correction for rainrate retrievals from satellite passive microwave sensors
Short, David A.
Rainrates retrieved from past and present satellite-borne microwave sensors are affected by a fundamental remote sensing problem. Sensor fields-of-view are typically large enough to encompass substantial rainrate variability, whereas the retrieval algorithms, based on radiative transfer calculations, show a non-linear relationship between rainrate and microwave brightness temperature. Retrieved rainrates are systematically too low. A statistical model of the bias problem shows that bias correction factors depend on the probability distribution of instantaneous rainrate and on the average thickness of the rain layer.
Radar Rainfall Bias Correction based on Deep Learning Approach
Song, Yang; Han, Dawei; Rico-Ramirez, Miguel A.
Radar rainfall measurement errors can be considerably attributed to various sources including intricate synoptic regimes. Temperature, humidity and wind are typically acknowledged as critical meteorological factors in inducing the precipitation discrepancies aloft and on the ground. The conventional practices mainly use the radar-gauge or geostatistical techniques by direct weighted interpolation algorithms as bias correction schemes whereas rarely consider the atmospheric effects. This study aims to comprehensively quantify those meteorological elements' impacts on radar-gauge rainfall bias correction based on a deep learning approach. The deep learning approach employs deep convolutional neural networks to automatically extract three-dimensional meteorological features for target recognition based on high range resolution profiles. The complex nonlinear relationships between input and target variables can be implicitly detected by such a scheme, which is validated on the test dataset. The proposed bias correction scheme is expected to be a promising improvement in systematically minimizing the synthesized atmospheric effects on rainfall discrepancies between radar and rain gauges, which can be useful in many meteorological and hydrological applications (e.g., real-time flood forecasting) especially for regions with complex atmospheric conditions.
Local linear density estimation for filtered survival data, with bias correction
DEFF Research Database (Denmark)
Nielsen, Jens Perch; Tanggaard, Carsten; Jones, M.C.
it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias-correction methods...... within our framework. The multiplicative bias-correction method proves to be the best in a simulation study comparing the performance of the considered estimators. An example concerning old-age mortality demonstrates the importance of the improvements provided....
Local Linear Density Estimation for Filtered Survival Data with Bias Correction
DEFF Research Database (Denmark)
Tanggaard, Carsten; Nielsen, Jens Perch; Jones, M.C.
it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias correction methods...... within our framework. The multiplicative bias correction method proves to be best in a simulation study comparing the performance of the considered estimators. An example concerning old age mortality demonstrates the importance of the improvements provided....
Nonlinear bias analysis and correction of microwave temperature sounder observations for FY-3C meteorological satellite
Hu, Taiyang; Lv, Rongchuan; Jin, Xu; Li, Hao; Chen, Wenxin
The nonlinear bias analysis and correction of receiving channels in Chinese FY-3C meteorological satellite Microwave Temperature Sounder (MWTS) is a key technology of data assimilation for satellite radiance data. The thermal-vacuum chamber calibration data acquired from the MWTS can be analyzed to evaluate the instrument performance, including radiometric temperature sensitivity, channel nonlinearity and calibration accuracy. Especially, the nonlinearity parameters due to imperfect square-law detectors will be calculated from calibration data and further used to correct the nonlinear bias contributions of microwave receiving channels. Based upon the operational principles and thermalvacuum chamber calibration procedures of MWTS, this paper mainly focuses on the nonlinear bias analysis and correction methods for improving the calibration accuracy of the important instrument onboard FY-3C meteorological satellite, from the perspective of theoretical and experimental studies. Furthermore, a series of original results are presented to demonstrate the feasibility and significance of the methods.
Correction of technical bias in clinical microarray data improves concordance with known biological information
DEFF Research Database (Denmark)
Eklund, Aron Charles; Szallasi, Zoltan Imre
The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data...... sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets....
Correcting estimators of theta and Tajima's D for ascertainment biases caused by the single-nucleotide polymorphism discovery process
DEFF Research Database (Denmark)
Ramírez-Soriano, Anna; Nielsen, Rasmus
Most single-nucleotide polymorphism (SNP) data suffer from an ascertainment bias caused by the process of SNP discovery followed by SNP genotyping. The final genotyped data are biased toward an excess of common alleles compared to directly sequenced data, making standard genetic methods of analysis...... the variances and covariances of these estimators and provide a corrected version of Tajima's D statistic. We reanalyze a human genomewide SNP data set and find substantial differences in the results with or without ascertainment bias correction....
Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study
International Nuclear Information System (INIS)
Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q.; Ducote, Justin L.; Su, Min-Ying; Molloi, Sabee
Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson'sr, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson'sr increased from 0.86 to 0.92 with the bias field correction
Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset
Lange, Stefan
Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.
Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset
Directory of Open Access Journals (Sweden)
S. Lange
Full Text Available Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds. Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016 rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011 data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016. This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.
Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.
Ding, Huanjun; Johnson, Travis; Lin, Muqing; Le, Huy Q; Ducote, Justin L; Su, Min-Ying; Molloi, Sabee
Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. The investigated CLIC method
Fat fraction bias correction using T1 estimates and flip angle mapping.
Yang, Issac Y; Cui, Yifan; Wiens, Curtis N; Wade, Trevor P; Friesen-Waldner, Lanette J; McKenzie, Charles A
To develop a new method of reducing T1 bias in proton density fat fraction (PDFF) measured with iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL). PDFF maps reconstructed from high flip angle IDEAL measurements were simulated and acquired from phantoms and volunteer L4 vertebrae. T1 bias was corrected using a priori T1 values for water and fat, both with and without flip angle correction. Signal-to-noise ratio (SNR) maps were used to measure precision of the reconstructed PDFF maps. PDFF measurements acquired using small flip angles were then compared to both sets of corrected large flip angle measurements for accuracy and precision. Simulations show similar results in PDFF error between small flip angle measurements and corrected large flip angle measurements as long as T1 estimates were within one standard deviation from the true value. Compared to low flip angle measurements, phantom and in vivo measurements demonstrate better precision and accuracy in PDFF measurements if images were acquired at a high flip angle, with T1 bias corrected using T1 estimates and flip angle mapping. T1 bias correction of large flip angle acquisitions using estimated T1 values with flip angle mapping yields fat fraction measurements of similar accuracy and superior precision compared to low flip angle acquisitions. Copyright © 2013 Wiley Periodicals, Inc.
Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases.
Darbani, Behrooz; Stewart, C Neal; Noeparvar, Shahin; Borg, Søren
This report investigates for the first time the potential inter-treatment bias source of cell number for gene expression studies. Cell-number bias can affect gene expression analysis when comparing samples with unequal total cellular RNA content or with different RNA extraction efficiencies. For maximal reliability of analysis, therefore, comparisons should be performed at the cellular level. This could be accomplished using an appropriate correction method that can detect and remove the inter-treatment bias for cell-number. Based on inter-treatment variations of reference genes, we introduce an analytical approach to examine the suitability of correction methods by considering the inter-treatment bias as well as the inter-replicate variance, which allows use of the best correction method with minimum residual bias. Analyses of RNA sequencing and microarray data showed that the efficiencies of correction methods are influenced by the inter-treatment bias as well as the inter-replicate variance. Therefore, we recommend inspecting both of the bias sources in order to apply the most efficient correction method. As an alternative correction strategy, sequential application of different correction approaches is also advised. Copyright © 2014 Elsevier B.V. All rights reserved.
The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.
Duan, Yuping; Chang, Huibin; Huang, Weimin; Zhou, Jiayin; Lu, Zhongkang; Wu, Chunlin
We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.
Discharge simulations performed with a hydrological model using bias corrected regional climate model input
Pelt, van S.C.; Kabat, P.; Maat, ter H.W.; Hurk, van den B.J.J.M.; Weerts, A.H.
Studies have demonstrated that precipitation on Northern Hemisphere mid-latitudes has increased in the last decades and that it is likely that this trend will continue. This will have an influence on discharge of the river Meuse. The use of bias correction methods is important when the effect of
Potential of bias correction for downscaling passive microwave and soil moisture data
Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...
Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System
Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances
In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.
[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].
Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao
An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.
Correction of Selection Bias in Survey Data: Is the Statistical Cure Worse Than the Bias?
Hanley, James A
In previous articles in the American Journal of Epidemiology (Am J Epidemiol. 2013;177(5):431-442) and American Journal of Public Health (Am J Public Health. 2013;103(10):1895-1901), Masters et al. reported age-specific hazard ratios for the contrasts in mortality rates between obesity categories. They corrected the observed hazard ratios for selection bias caused by what they postulated was the nonrepresentativeness of the participants in the National Health Interview Study that increased with age, obesity, and ill health. However, it is possible that their regression approach to remove the alleged bias has not produced, and in general cannot produce, sensible hazard ratio estimates. First, we must consider how many nonparticipants there might have been in each category of obesity and of age at entry and how much higher the mortality rates would have to be in nonparticipants than in participants in these same categories. What plausible set of numerical values would convert the ("biased") decreasing-with-age hazard ratios seen in the data into the ("unbiased") increasing-with-age ratios that they computed? Can these values be encapsulated in (and can sensible values be recovered from) one additional internal variable in a regression model? Second, one must examine the age pattern of the hazard ratios that have been adjusted for selection. Without the correction, the hazard ratios are attenuated with increasing age. With it, the hazard ratios at older ages are considerably higher, but those at younger ages are well below one. Third, one must test whether the regression approach suggested by Masters et al. would correct the nonrepresentativeness that increased with age and ill health that I introduced into real and hypothetical data sets. I found that the approach did not recover the hazard ratio patterns present in the unselected data sets: the corrections overshot the target at older ages and undershot it at lower ages.
Generalized subspace correction methods
Energy Technology Data Exchange (ETDEWEB)
Kolm, P. [Royal Institute of Technology, Stockholm (Sweden); Arbenz, P.; Gander, W. [Eidgenoessiche Technische Hochschule, Zuerich (Switzerland)
A fundamental problem in scientific computing is the solution of large sparse systems of linear equations. Often these systems arise from the discretization of differential equations by finite difference, finite volume or finite element methods. Iterative methods exploiting these sparse structures have proven to be very effective on conventional computers for a wide area of applications. Due to the rapid development and increasing demand for the large computing powers of parallel computers, it has become important to design iterative methods specialized for these new architectures.
Detecting and correcting for publication bias in meta-analysis - A truncated normal distribution approach.
Zhu, Qiaohao; Carriere, K C
Publication bias can significantly limit the validity of meta-analysis when trying to draw conclusion about a research question from independent studies. Most research on detection and correction for publication bias in meta-analysis focus mainly on funnel plot-based methodologies or selection models. In this paper, we formulate publication bias as a truncated distribution problem, and propose new parametric solutions. We develop methodologies of estimating the underlying overall effect size and the severity of publication bias. We distinguish the two major situations, in which publication bias may be induced by: (1) small effect size or (2) large p-value. We consider both fixed and random effects models, and derive estimators for the overall mean and the truncation proportion. These estimators will be obtained using maximum likelihood estimation and method of moments under fixed- and random-effects models, respectively. We carried out extensive simulation studies to evaluate the performance of our methodology, and to compare with the non-parametric Trim and Fill method based on funnel plot. We find that our methods based on truncated normal distribution perform consistently well, both in detecting and correcting publication bias under various situations.
HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter
Directory of Open Access Journals (Sweden)
Qingjiao Sun
Full Text Available Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR pathological image enhancement method based on improved bias field correction and guided image filter (GIF. Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.
Image-guided regularization level set evolution for MR image segmentation and bias field correction.
Wang, Lingfeng; Pan, Chunhong
Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches. Copyright © 2014 Elsevier Inc. All rights reserved.
Sequence-specific bias correction for RNA-seq data using recurrent neural networks.
Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru
The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.
Application of bias correction methods to improve U{sub 3}Si{sub 2} sample preparation for quantitative analysis by WDXRF
Energy Technology Data Exchange (ETDEWEB)
Scapin, Marcos A.; Guilhen, Sabine N.; Azevedo, Luciana C. de; Cotrim, Marycel E.B.; Pires, Maria Ap. F., E-mail: email@example.com, E-mail: firstname.lastname@example.com, E-mail: email@example.com, E-mail: email@example.com, E-mail: email@example.com [Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP), São Paulo, SP (Brazil)
The determination of silicon (Si), total uranium (U) and impurities in uranium-silicide (U{sub 3}Si{sub 2}) samples by wavelength dispersion X-ray fluorescence technique (WDXRF) has been already validated and is currently implemented at IPEN's X-Ray Fluorescence Laboratory (IPEN-CNEN/SP) in São Paulo, Brazil. Sample preparation requires the use of approximately 3 g of H{sub 3}BO{sub 3} as sample holder and 1.8 g of U{sub 3}Si{sub 2}. However, because boron is a neutron absorber, this procedure precludes U{sub 3}Si{sub 2} sample's recovery, which, in time, considering routinely analysis, may account for significant unusable uranium waste. An estimated average of 15 samples per month are expected to be analyzed by WDXRF, resulting in approx. 320 g of U{sub 3}Si{sub 2} that would not return to the nuclear fuel cycle. This not only impacts in production losses, but generates another problem: radioactive waste management. The purpose of this paper is to present the mathematical models that may be applied for the correction of systematic errors when H{sub 3}BO{sub 3} sample holder is substituted by cellulose-acetate {[C_6H_7O_2(OH)_3_-_m(OOCCH_3)m], m = 0∼3}, thus enabling U{sub 3}Si{sub 2} sample's recovery. The results demonstrate that the adopted mathematical model is statistically satisfactory, allowing the optimization of the procedure. (author)
Measurement of the $B^-$ lifetime using a simulation free approach for trigger bias correction
Energy Technology Data Exchange (ETDEWEB)
Aaltonen, T.; /Helsinki Inst. of Phys.; Adelman, J.; /Chicago U., EFI; Alvarez Gonzalez, B.; /Cantabria Inst. of Phys.; Amerio, S.; /INFN, Padua; Amidei, D.; /Michigan U.; Anastassov, A.; /Northwestern U.; Annovi, A.; /Frascati; Antos, J.; /Comenius U.; Apollinari, G.; /Fermilab; Appel, J.; /Fermilab; Apresyan, A.; /Purdue U. /Waseda U.
The collection of a large number of B hadron decays to hadronic final states at the CDF II detector is possible due to the presence of a trigger that selects events based on track impact parameters. However, the nature of the selection requirements of the trigger introduces a large bias in the observed proper decay time distribution. A lifetime measurement must correct for this bias and the conventional approach has been to use a Monte Carlo simulation. The leading sources of systematic uncertainty in the conventional approach are due to differences between the data and the Monte Carlo simulation. In this paper they present an analytic method for bias correction without using simulation, thereby removing any uncertainty between data and simulation. This method is presented in the form of a measurement of the lifetime of the B{sup -} using the mode B{sup -} {yields} D{sup 0}{pi}{sup -}. The B{sup -} lifetime is measured as {tau}{sub B{sup -}} = 1.663 {+-} 0.023 {+-} 0.015 ps, where the first uncertainty is statistical and the second systematic. This new method results in a smaller systematic uncertainty in comparison to methods that use simulation to correct for the trigger bias.
Measurement of the B- lifetime using a simulation free approach for trigger bias correction
International Nuclear Information System (INIS)
The collection of a large number of B hadron decays to hadronic final states at the CDF II detector is possible due to the presence of a trigger that selects events based on track impact parameters. However, the nature of the selection requirements of the trigger introduces a large bias in the observed proper decay time distribution. A lifetime measurement must correct for this bias and the conventional approach has been to use a Monte Carlo simulation. The leading sources of systematic uncertainty in the conventional approach are due to differences between the data and the Monte Carlo simulation. In this paper they present an analytic method for bias correction without using simulation, thereby removing any uncertainty between data and simulation. This method is presented in the form of a measurement of the lifetime of the B - using the mode B - → D 0 π - . The B - lifetime is measured as τ B# sup -# = 1.663 ± 0.023 ± 0.015 ps, where the first uncertainty is statistical and the second systematic. This new method results in a smaller systematic uncertainty in comparison to methods that use simulation to correct for the trigger bias.
Bias of shear wave elasticity measurements in thin layer samples and a simple correction strategy.
Mo, Jianqiang; Xu, Hao; Qiang, Bo; Giambini, Hugo; Kinnick, Randall; An, Kai-Nan; Chen, Shigao; Luo, Zongping
Shear wave elastography (SWE) is an emerging technique for measuring biological tissue stiffness. However, the application of SWE in thin layer tissues is limited by bias due to the influence of geometry on measured shear wave speed. In this study, we investigated the bias of Young's modulus measured by SWE in thin layer gelatin-agar phantoms, and compared the result with finite element method and Lamb wave model simulation. The result indicated that the Young's modulus measured by SWE decreased continuously when the sample thickness decreased, and this effect was more significant for smaller thickness. We proposed a new empirical formula which can conveniently correct the bias without the need of using complicated mathematical modeling. In summary, we confirmed the nonlinear relation between thickness and Young's modulus measured by SWE in thin layer samples, and offered a simple and practical correction strategy which is convenient for clinicians to use.
[Inverse probability weighting (IPW) for evaluating and "correcting" selection bias].
Narduzzi, Silvia; Golini, Martina Nicole; Porta, Daniela; Stafoggia, Massimo; Forastiere, Francesco
the Inverse probability weighting (IPW) is a methodology developed to account for missingness and selection bias caused by non-randomselection of observations, or non-random lack of some information in a subgroup of the population. to provide an overview of IPW methodology and an application in a cohort study of the association between exposure to traffic air pollution (nitrogen dioxide, NO₂) and 7-year children IQ. this methodology allows to correct the analysis by weighting the observations with the probability of being selected. The IPW is based on the assumption that individual information that can predict the probability of inclusion (non-missingness) are available for the entire study population, so that, after taking account of them, we can make inferences about the entire target population starting from the nonmissing observations alone.The procedure for the calculation is the following: firstly, we consider the entire population at study and calculate the probability of non-missing information using a logistic regression model, where the response is the nonmissingness and the covariates are its possible predictors.The weight of each subject is given by the inverse of the predicted probability. Then the analysis is performed only on the non-missing observations using a weighted model. IPW is a technique that allows to embed the selection process in the analysis of the estimates, but its effectiveness in "correcting" the selection bias depends on the availability of enough information, for the entire population, to predict the non-missingness probability. In the example proposed, the IPW application showed that the effect of exposure to NO2 on the area of verbal intelligence quotient of children is stronger than the effect showed from the analysis performed without regard to the selection processes.
Decay correction methods in dynamic PET studies
International Nuclear Information System (INIS)
Chen, K.; Reiman, E.; Lawson, M.
In order to reconstruct positron emission tomography (PET) images in quantitative dynamic studies, the data must be corrected for radioactive decay. One of the two commonly used methods ignores physiological processes including blood flow that occur at the same time as radioactive decay; the other makes incorrect use of time-accumulated PET counts. In simulated dynamic PET studies using 11 C-acetate and 18 F-fluorodeoxyglucose (FDG), these methods are shown to result in biased estimates of the time-activity curve (TAC) and model parameters. New methods described in this article provide significantly improved parameter estimates in dynamic PET studies
Experimenter Confirmation Bias and the Correction of Science Misconceptions
Allen, Michael; Coole, Hilary
This paper describes a randomised educational experiment (n = 47) that examined two different teaching methods and compared their effectiveness at correcting one science misconception using a sample of trainee primary school teachers. The treatment was designed to promote engagement with the scientific concept by eliciting emotional responses from…
Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru
Manzanas, R.; Gutiérrez, J. M.
This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981-2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile-quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.
Bias-correction and Spatial Disaggregation for Climate Change Impact Assessments at a basin scale
Nyunt, Cho; Koike, Toshio; Yamamoto, Akio; Nemoto, Toshihoro; Kitsuregawa, Masaru
Basin-scale climate change impact studies mainly rely on general circulation models (GCMs) comprising the related emission scenarios. Realistic and reliable data from GCM is crucial for national scale or basin scale impact and vulnerability assessments to build safety society under climate change. However, GCM fail to simulate regional climate features due to the imprecise parameterization schemes in atmospheric physics and coarse resolution scale. This study describes how to exclude some unsatisfactory GCMs with respect to focused basin, how to minimize the biases of GCM precipitation through statistical bias correction and how to cover spatial disaggregation scheme, a kind of downscaling, within in a basin. GCMs rejection is based on the regional climate features of seasonal evolution as a bench mark and mainly depends on spatial correlation and root mean square error of precipitation and atmospheric variables over the target region. Global Precipitation Climatology Project (GPCP) and Japanese 25-uear Reanalysis Project (JRA-25) are specified as references in figuring spatial pattern and error of GCM. Statistical bias-correction scheme comprises improvements of three main flaws of GCM precipitation such as low intensity drizzled rain days with no dry day, underestimation of heavy rainfall and inter-annual variability of local climate. Biases of heavy rainfall are conducted by generalized Pareto distribution (GPD) fitting over a peak over threshold series. Frequency of rain day error is fixed by rank order statistics and seasonal variation problem is solved by using a gamma distribution fitting in each month against insi-tu stations vs. corresponding GCM grids. By implementing the proposed bias-correction technique to all insi-tu stations and their respective GCM grid, an easy and effective downscaling process for impact studies at the basin scale is accomplished. The proposed method have been examined its applicability to some of the basins in various climate
Correcting Biases in a lower resolution global circulation model with data assimilation
Canter, Martin; Barth, Alexander
With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model's equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have
Length bias correction in one-day cross-sectional assessments - The nutritionDay study.
Frantal, Sophie; Pernicka, Elisabeth; Hiesmayr, Michael; Schindler, Karin; Bauer, Peter
A major problem occurring in cross-sectional studies is sampling bias. Length of hospital stay (LOS) differs strongly between patients and causes a length bias as patients with longer LOS are more likely to be included and are therefore overrepresented in this type of study. To adjust for the length bias higher weights are allocated to patients with shorter LOS. We determined the effect of length-bias adjustment in two independent populations. Length-bias correction is applied to the data of the nutritionDay project, a one-day multinational cross-sectional audit capturing data on disease and nutrition of patients admitted to hospital wards with right-censoring after 30 days follow-up. We applied the weighting method for estimating the distribution function of patient baseline variables based on the method of non-parametric maximum likelihood. Results are validated using data from all patients admitted to the General Hospital of Vienna between 2005 and 2009, where the distribution of LOS can be assumed to be known. Additionally, a simplified calculation scheme for estimating the adjusted distribution function of LOS is demonstrated on a small patient example. The crude median (lower quartile; upper quartile) LOS in the cross-sectional sample was 14 (8; 24) and decreased to 7 (4; 12) when adjusted. Hence, adjustment for length bias in cross-sectional studies is essential to get appropriate estimates. Copyright © 2015 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies
Theis, Fabian J.
Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464
Significance of Bias Correction in Drought Frequency and Scenario Analysis Based on Climate Models
Aryal, Y.; Zhu, J.
Assessment of future drought characteristics is difficult as climate models usually have bias in simulating precipitation frequency and intensity. To overcome this limitation, output from climate models need to be bias corrected based on the specific purpose of applications. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we investigate the performance of three widely used bias correction techniques: (1) monthly bias correction (MBC), (2) nested bias correction (NBC), and (3) equidistance quantile mapping (EQM) The effect of bias correction in future scenario of drought frequency is also analyzed. The characteristics of drought are investigated in terms of frequency and severity in nine representative locations in different climatic regions across the United States using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used as the means to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Preserving mean and standard deviation is essential for climate models in drought frequency analysis. RCM biases both have regional and timescale dependence. Different timescale of input precipitation in the bias corrections show similar results. Drought frequency obtained from the RCM future (2040-2070) scenarios is compared with that from the historical simulations. The changes in drought characteristics occur in all climatic regions. The relative changes in drought frequency in future scenario in relation to
Correct acceptance weighs more than correct rejection: a decision bias induced by question framing.
Kareev, Yaakov; Trope, Yaacov
We propose that in attempting to detect whether an effect exists or not, people set their decision criterion so as to increase the number of hits and decrease the number of misses, at the cost of increasing false alarms and decreasing correct rejections. As a result, we argue, if one of two complementary events is framed as the positive response to a question and the other as the negative response, people will tend to predict the former more often than the latter. Performance in a prediction task with symmetric payoffs and equal base rates supported our proposal. Positive responses were indeed more prevalent than negative responses, irrespective of the phrasing of the question. The bias, slight but consistent and significant, was evident from early in a session and then remained unchanged to the end. A regression analysis revealed that, in addition, individuals' decision criteria reflected their learning experiences, with the weight of hits being greater than that of correct rejections.
Correction for dynamic bias error in transmission measurements of void fraction
International Nuclear Information System (INIS)
Andersson, P.; Sundén, E. Andersson; Svärd, S. Jacobsson; Sjöstrand, H.
Dynamic bias errors occur in transmission measurements, such as X-ray, gamma, or neutron radiography or tomography. This is observed when the properties of the object are not stationary in time and its average properties are assessed. The nonlinear measurement response to changes in transmission within the time scale of the measurement implies a bias, which can be difficult to correct for. A typical example is the tomographic or radiographic mapping of void content in dynamic two-phase flow systems. In this work, the dynamic bias error is described and a method to make a first-order correction is derived. A prerequisite for this method is variance estimates of the system dynamics, which can be obtained using high-speed, time-resolved data acquisition. However, in the absence of such acquisition, a priori knowledge might be used to substitute the time resolved data. Using synthetic data, a void fraction measurement case study has been simulated to demonstrate the performance of the suggested method. The transmission length of the radiation in the object under study and the type of fluctuation of the void fraction have been varied. Significant decreases in the dynamic bias error were achieved to the expense of marginal decreases in precision.
Directory of Open Access Journals (Sweden)
Sandra Mourato
Full Text Available Regional climate models provided precipitation and temperature time series for control (1961–1990 and scenario (2071–2100 periods. At southern Portu gal, the climate models in the control period systematically present higher temp eratures and lower precipitation than the observations. Therefore, the direct inpu t of climate model data into hydrological models might result in more severe scenarios for future water availability. Three bias correction methods (Delta Change, Dire ct Forcing and Hybrid are analysed and their performances in water availability impac t studies are assessed. The Delta Change method assumes that the observed series variab ility is maintained in the scenario period and is corrected by the evolution predicted by the climate models. The Direct Forcing method maintains the scenario series variabi lity, which is corrected by the bias found in the control period, and the Hybrid method maintains the control model series variability, which is corrected by the bias found in the control period and by the evolution predicted by the climate models. To assess the climate impacts in the water resources expected for the scenario period, a physically based spatially distributed hydrological model, SHETRAN, is used for runoff pro jections in a southern Portugal basin. The annual and seasonal runoff shows a runoff d ecrease in the scenario period, increasing the water shor tage that is already experienc ed. The overall annual reduction varies between –80% and –35%. In general, the results show that the runoff reductions obtained with climate models corrected with the Delt a Change method are highest but with a narrow range that varies between –80% and –5 2%.
Complex differential variance angiography with noise-bias correction for optical coherence tomography of the retina.
Braaf, Boy; Donner, Sabine; Nam, Ahhyun S; Bouma, Brett E; Vakoc, Benjamin J
Complex differential variance (CDV) provides phase-sensitive angiographic imaging for optical coherence tomography (OCT) with immunity to phase-instabilities of the imaging system and small-scale axial bulk motion. However, like all angiographic methods, measurement noise can result in erroneous indications of blood flow that confuse the interpretation of angiographic images. In this paper, a modified CDV algorithm that corrects for this noise-bias is presented. This is achieved by normalizing the CDV signal by analytically derived upper and lower limits. The noise-bias corrected CDV algorithm was implemented into an experimental 1 μm wavelength OCT system for retinal imaging that used an eye tracking scanner laser ophthalmoscope at 815 nm for compensation of lateral eye motions. The noise-bias correction improved the CDV imaging of the blood flow in tissue layers with a low signal-to-noise ratio and suppressed false indications of blood flow outside the tissue. In addition, the CDV signal normalization suppressed noise induced by galvanometer scanning errors and small-scale lateral motion. High quality cross-section and motion-corrected en face angiograms of the retina and choroid are presented.
Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.
Marcos, Raül; Llasat, Ma Carmen; Quintana-Seguí, Pere; Turco, Marco
In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter. Copyright © 2017 Elsevier B.V. All rights reserved.
Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.
Jeon, Jihyoun; Hsu, Li; Gorfine, Malka
Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.
A simple correction to remove the bias of the gini coefficient due to grouping
T.G.M. van Ourti (Tom); Ph. Clarke (Philip)
textabstractAbstract-We propose a first-order bias correction term for the Gini index to reduce the bias due to grouping. It depends on only the number of individuals in each group and is derived from a measurement error framework. We also provide a formula for the remaining second-order bias. Both
Bias correction for selecting the minimal-error classifier from many machine learning models.
Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C
Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. email@example.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com.
Bias Correction in a Stable AD (1,1) Model: Weak versus Strong Exogeneity
van Giersbergen, N.P.A.
This paper compares the behaviour of a bias-corrected estimator assuming strongly exogenous regressors to the behaviour of a bias-corrected estimator assuming weakly exogenous regressors, when in fact the marginal model contains a feedback mechanism. To this end, the effects of a feedback mechanism
Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models
Raykov, Tenko
A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…
Bias field inconsistency correction of motion-scattered multislice MRI for improved 3D image reconstruction.
Kim, Kio; Habas, Piotr A; Rajagopalan, Vidya; Scott, Julia A; Corbett-Detig, James M; Rousseau, Francois; Barkovich, A James; Glenn, Orit A; Studholme, Colin
A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.
Implementation of Coupled Skin Temperature Analysis and Bias Correction in a Global Atmospheric Data Assimilation System
Radakovich, Jon; Bosilovich, M.; Chern, Jiun-dar; daSilva, Arlindo
The NASA/NCAR Finite Volume GCM (fvGCM) with the NCAR CLM (Community Land Model) version 2.0 was integrated into the NASA/GMAO Finite Volume Data Assimilation System (fvDAS). A new method was developed for coupled skin temperature assimilation and bias correction where the analysis increment and bias correction term is passed into the CLM2 and considered a forcing term in the solution to the energy balance. For our purposes, the fvDAS CLM2 was run at 1 deg. x 1.25 deg. horizontal resolution with 55 vertical levels. We assimilate the ISCCP-DX (30 km resolution) surface temperature product. The atmospheric analysis was performed 6-hourly, while the skin temperature analysis was performed 3-hourly. The bias correction term, which was updated at the analysis times, was added to the skin temperature tendency equation at every timestep. In this presentation, we focus on the validation of the surface energy budget at the in situ reference sites for the Coordinated Enhanced Observation Period (CEOP). We will concentrate on sites that include independent skin temperature measurements and complete energy budget observations for the month of July 2001. In addition, MODIS skin temperature will be used for validation. Several assimilations were conducted and preliminary results will be presented.
CD-SEM real time bias correction using reference metrology based modeling
Ukraintsev, V.; Banke, W.; Zagorodnev, G.; Archie, C.; Rana, N.; Pavlovsky, V.; Smirnov, V.; Briginas, I.; Katnani, A.; Vaid, A.
Accuracy of patterning impacts yield, IC performance and technology time to market. Accuracy of patterning relies on optical proximity correction (OPC) models built using CD-SEM inputs and intra die critical dimension (CD) control based on CD-SEM. Sub-nanometer measurement uncertainty (MU) of CD-SEM is required for current technologies. Reported design and process related bias variation of CD-SEM is in the range of several nanometers. Reference metrology and numerical modeling are used to correct SEM. Both methods are slow to be used for real time bias correction. We report on real time CD-SEM bias correction using empirical models based on reference metrology (RM) data. Significant amount of currently untapped information (sidewall angle, corner rounding, etc.) is obtainable from SEM waveforms. Using additional RM information provided for specific technology (design rules, materials, processes) CD extraction algorithms can be pre-built and then used in real time for accurate CD extraction from regular CD-SEM images. The art and challenge of SEM modeling is in finding robust correlation between SEM waveform features and bias of CD-SEM as well as in minimizing RM inputs needed to create accurate (within the design and process space) model. The new approach was applied to improve CD-SEM accuracy of 45 nm GATE and 32 nm MET1 OPC 1D models. In both cases MU of the state of the art CD-SEM has been improved by 3x and reduced to a nanometer level. Similar approach can be applied to 2D (end of line, contours, etc.) and 3D (sidewall angle, corner rounding, etc.) cases.
Non-iterative relative bias correction for 3D reconstruction of in utero fetal brain MR imaging.
Kim, Kio; Habas, Piotr; Rajagopalan, Vidya; Scott, Julia; Corbett-Detig, James; Rousseau, Francois; Glenn, Orit; Barkovich, James; Studholme, Colin
The slice intersection motion correction (SIMC) method is a powerful tool to compensate for motion that occurs during in utero acquisition of the multislice magnetic resonance (MR) images of the human fetal brain. The SIMC method makes use of the slice intersection intensity profiles of orthogonally planned slice pairs to simultaneously correct for the relative motion occurring between all the acquired slices. This approach is based on the assumption that the bias field is consistent between slices. However, for some clinical studies where there is a strong bias field combined with significant fetal motion relative to the coils, this assumption is broken and the resulting motion estimate and the reconstruction to a 3D volume can both contain errors. In this work, we propose a method to correct for the relative differences in bias field between all slice pairs. For this, we define the energy function as the mean square difference of the intersection profiles, that is then minimized with respect to the bias field parameters of the slices. A non iterative method which considers the relative bias between each slice simultaneously is used to efficiently remove inconsistencies. The method, when tested on synthetic simulations and actual clinical imaging studies where bias was an issue, brought a significant improvement to the final reconstructed image.
Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction.
Malkyarenko, Dariya I; Chenevert, Thomas L
To describe an efficient procedure to empirically characterize gradient nonlinearity and correct for the corresponding apparent diffusion coefficient (ADC) bias on a clinical magnetic resonance imaging (MRI) scanner. Spatial nonlinearity scalars for individual gradient coils along superior and right directions were estimated via diffusion measurements of an isotropicic e-water phantom. Digital nonlinearity model from an independent scanner, described in the literature, was rescaled by system-specific scalars to approximate 3D bias correction maps. Correction efficacy was assessed by comparison to unbiased ADC values measured at isocenter. Empirically estimated nonlinearity scalars were confirmed by geometric distortion measurements of a regular grid phantom. The applied nonlinearity correction for arbitrarily oriented diffusion gradients reduced ADC bias from 20% down to 2% at clinically relevant offsets both for isotropic and anisotropic media. Identical performance was achieved using either corrected diffusion-weighted imaging (DWI) intensities or corrected b-values for each direction in brain and ice-water. Direction-average trace image correction was adequate only for isotropic medium. Empiric scalar adjustment of an independent gradient nonlinearity model adequately described DWI bias for a clinical scanner. Observed efficiency of implemented ADC bias correction quantitatively agreed with previous theoretical predictions and numerical simulations. The described procedure provides an independent benchmark for nonlinearity bias correction of clinical MRI scanners.
A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations
Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish
Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.
Retrospective analysis of Mexican National Addictions Survey, 2008. Bias identification and correction.
Directory of Open Access Journals (Sweden)
Martín Romero-Martínez
Full Text Available Objective. To determine the presence of bias on the estimation of the consumption sometime in life of alcohol, tobacco or illegal drugs and inhalable substances, and to propose a correction for this in the case it is present. Materials and methods. Mexican National Addictions Surveys (NAS 2002, 2008, and 2011 were analyzed to compare population estimations of consumption sometime in life of tobacco, alcohol or illegal drugs and inhalable substances. A couple of alternative approaches for bias correction were developed. Results. Estimated national prevalences of consumption sometime in life of alcohol and tobacco in the NAS 2008 are not plausible. There was no evidence of bias on the consumption sometime in life of illegal drugs and inhalable substances. New estimations for tobacco and alcohol consumption sometime in life were made, which resulted in plausible values when compared to other data available. Conclusion. Future analyses regarding tobacco and alcohol using NAS 2008 data will have to rely on these newly generated data weights, that are able to reproduce the new (plausible estimations.
Bias and equivalence of the Strengths Use and Deficit Correction Questionnaire
Directory of Open Access Journals (Sweden)
Crizelle Els
Full Text Available Orientation: For optimal outcomes, it is suggested that employees receive support from their organisation to use their strengths and improve their deficits. Employees also engage in proactive behaviour to use their strengths and improve their deficits. Following this conversation, the Strengths Use and Deficit Correction Questionnaire (SUDCO was developed. However, the cultural suitability of the SUDCO has not been confirmed. Research purpose: The purpose of this study was to examine the bias and structural equivalence of the SUDCO. Motivation for the study: In a diverse cultural context such as South Africa, it is important to establish that a similar score on a psychological test has the same psychological meaning across ethnic groups. Research design, approach and method: A cross-sectional survey design was followed to collect data among a convenience sample of 858 employees from various occupational sectors in South Africa. Main findings: Confirmatory multigroup analysis was used to test for item and construct bias. None of the items were biased, neither uniform nor non-uniform. The most restrictive model accounted for similarities in weights, intercepts and means; only residuals were different. Practical/managerial implications: The results suggest that the SUDCO is suitable for use among the major ethnic groups included in this study. These results increase the probability that future studies with the SUDCO among other ethnic groups will be unbiased and equivalent. Contribution: This study contributed to existing literature because no previous research has assessed the bias and equivalence of the SUDCO among ethnic groups in South Africa.
MRI non-uniformity correction through interleaved bias estimation and B-spline deformation with a template.
Fletcher, E; Carmichael, O; Decarli, C
We propose a template-based method for correcting field inhomogeneity biases in magnetic resonance images (MRI) of the human brain. At each algorithm iteration, the update of a B-spline deformation between an unbiased template image and the subject image is interleaved with estimation of a bias field based on the current template-to-image alignment. The bias field is modeled using a spatially smooth thin-plate spline interpolation based on ratios of local image patch intensity means between the deformed template and subject images. This is used to iteratively correct subject image intensities which are then used to improve the template-to-image deformation. Experiments on synthetic and real data sets of images with and without Alzheimer's disease suggest that the approach may have advantages over the popular N3 technique for modeling bias fields and narrowing intensity ranges of gray matter, white matter, and cerebrospinal fluid. This bias field correction method has the potential to be more accurate than correction schemes based solely on intrinsic image properties or hypothetical image intensity distributions.
MRI Non-Uniformity Correction Through Interleaved Bias Estimation and B-Spline Deformation with a Template*
Fletcher, E.; Carmichael, O.; DeCarli, C.
We propose a template-based method for correcting field inhomogeneity biases in magnetic resonance images (MRI) of the human brain. At each algorithm iteration, the update of a B-spline deformation between an unbiased template image and the subject image is interleaved with estimation of a bias field based on the current template-to-image alignment. The bias field is modeled using a spatially smooth thin-plate spline interpolation based on ratios of local image patch intensity means between the deformed template and subject images. This is used to iteratively correct subject image intensities which are then used to improve the template-to-image deformation. Experiments on synthetic and real data sets of images with and without Alzheimer's disease suggest that the approach may have advantages over the popular N3 technique for modeling bias fields and narrowing intensity ranges of gray matter, white matter, and cerebrospinal fluid. This bias field correction method has the potential to be more accurate than correction schemes based solely on intrinsic image properties or hypothetical image intensity distributions. PMID:23365843
Correcting bias in the rational polynomial coefficients of satellite imagery using thin-plate smoothing splines
Shen, Xiang; Liu, Bin; Li, Qing-Quan
The Rational Function Model (RFM) has proven to be a viable alternative to the rigorous sensor models used for geo-processing of high-resolution satellite imagery. Because of various errors in the satellite ephemeris and instrument calibration, the Rational Polynomial Coefficients (RPCs) supplied by image vendors are often not sufficiently accurate, and there is therefore a clear need to correct the systematic biases in order to meet the requirements of high-precision topographic mapping. In this paper, we propose a new RPC bias-correction method using the thin-plate spline modeling technique. Benefiting from its excellent performance and high flexibility in data fitting, the thin-plate spline model has the potential to remove complex distortions in vendor-provided RPCs, such as the errors caused by short-period orbital perturbations. The performance of the new method was evaluated by using Ziyuan-3 satellite images and was compared against the recently developed least-squares collocation approach, as well as the classical affine-transformation and quadratic-polynomial based methods. The results show that the accuracies of the thin-plate spline and the least-squares collocation approaches were better than the other two methods, which indicates that strong non-rigid deformations exist in the test data because they cannot be adequately modeled by simple polynomial-based methods. The performance of the thin-plate spline method was close to that of the least-squares collocation approach when only a few Ground Control Points (GCPs) were used, and it improved more rapidly with an increase in the number of redundant observations. In the test scenario using 21 GCPs (some of them located at the four corners of the scene), the correction residuals of the thin-plate spline method were about 36%, 37%, and 19% smaller than those of the affine transformation method, the quadratic polynomial method, and the least-squares collocation algorithm, respectively, which demonstrates
Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association
Grinde, Kelsey E.; Arbet, Jaron; Green, Alden; O'Connell, Michael; Valcarcel, Alessandra; Westra, Jason; Tintle, Nathan
To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as "winner's curse." We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures. PMID:28959274
Bias Corrections for Standardized Effect Size Estimates Used with Single-Subject Experimental Designs
Ugille, Maaike; Moeyaert, Mariola; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…
A new dynamical downscaling approach with GCM bias corrections and spectral nudging
Xu, Zhongfeng; Yang, Zong-Liang
To improve confidence in regional projections of future climate, a new dynamical downscaling (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.
Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange
Takaishi, Tetsuya
The realized stochastic volatility model has been introduced to estimate more accurate volatility by using both daily returns and realized volatility. The main advantage of the model is that no special bias-correction factor for the realized volatility is required a priori. Instead, the model introduces a bias-correction parameter responsible for the bias hidden in realized volatility. We empirically investigate the bias-correction parameter for realized volatilities calculated at various sampling frequencies for six stocks on the Tokyo Stock Exchange, and then show that the dynamic behavior of the bias-correction parameter as a function of sampling frequency is qualitatively similar to that of the Hansen-Lunde bias-correction factor although their values are substantially different. Under the stochastic diffusion assumption of the return dynamics, we investigate the accuracy of estimated volatilities by examining the standardized returns. We find that while the moments of the standardized returns from low-frequency realized volatilities are consistent with the expectation from the Gaussian variables, the deviation from the expectation becomes considerably large at high frequencies. This indicates that the realized stochastic volatility model itself cannot completely remove bias at high frequencies.
Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements.
Malyarenko, Dariya I; Ross, Brian D; Chenevert, Thomas L
Gradient nonlinearity of MRI systems leads to spatially dependent b-values and consequently high non-uniformity errors (10-20%) in apparent diffusion coefficient (ADC) measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Spatial dependence of nonlinearity correction terms accounts for the bulk (75-95%) of ADC bias for FA = 0.3-0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients. Copyright © 2013 Wiley Periodicals, Inc.
Bias Correction for the Maximum Likelihood Estimate of Ability. Research Report. ETS RR-05-15
Zhang, Jinming
Lord's bias function and the weighted likelihood estimation method are effective in reducing the bias of the maximum likelihood estimate of an examinee's ability under the assumption that the true item parameters are known. This paper presents simulation studies to determine the effectiveness of these two methods in reducing the bias when the item…
Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction?
Nguyen, Ha; Mehrotra, Rajeshwar; Sharma, Ashish
This work investigates the performance of four bias correction alternatives for representing persistence characteristics of precipitation across 37 General Circulation Models (GCMs) from the CMIP5 data archive. The first three correction approaches are the Simple Monthly Bias Correction (SMBC), Equidistance Quantile Mapping (EQM), and Nested Bias Correction (NBC), all of which operate in the time domain, with a focus on representing distributional and moment attributes in the observed precipitation record. The fourth approach corrects for the biases in high- and low-frequency variability or persistence of the GCM time series in the frequency domain and is named as Frequency-based Bias Correction (FBC). The Climatic Research Unit (CRU) gridded precipitation data covering the global land surface is used as a reference dataset. The assessment focusses on current and future means, variability, and drought-related characteristics at different temporal and spatial scales. For the current climate, all bias correction approaches perform reasonably well at the global scale by reproducing the observed precipitation statistics. For the future climate, focus is drawn on the agreement of the attributes across the GCMs considered. The inter-model difference/spread of each attribute across the GCMs is used as a measure of this agreement. Our results indicate that out of the four bias correction approaches used, FBC provides the lowest inter-model spreads, specifically for persistence attributes, over most regions/ parts over the global land surface. This has significant implications for most hydrological studies where the effect of low-frequency variability is of considerable importance.
Sampling of temporal networks: Methods and biases
Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter
Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.
An experimental verification of laser-velocimeter sampling bias and its correction
Johnson, D. A.; Modarress, D.; Owen, F. K.
The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.
The Detection and Correction of Bias in Student Ratings of Instruction.
Haladyna, Thomas; Hess, Robert K.
A Rasch model was used to detect and correct bias in Likert rating scales used to assess student perceptions of college teaching, using a database of ratings. Statistical corrections were significant, supporting the model's potential utility. Recommendations are made for a theoretical rationale and further research on the model. (Author/MSE)
Bias correction factors for near-Earth asteroids
Benedix, Gretchen K.; Mcfadden, Lucy Ann; Morrow, Esther M.; Fomenkova, Marina N.
Knowledge of the population size and physical characteristics (albedo, size, and rotation rate) of near-Earth asteroids (NEA's) is biased by observational selection effects which are functions of the population's intrinsic properties and the size of the telescope, detector sensitivity, and search strategy used. The NEA population is modeled in terms of orbital and physical elements: a, e, i, omega, Omega, M, albedo, and diameter, and an asteroid search program is simulated using actual telescope pointings of right ascension, declination, date, and time. The position of each object in the model population is calculated at the date and time of each telescope pointing. The program tests to see if that object is within the field of view (FOV = 8.75 degrees) of the telescope and above the limiting magnitude (V = +1.65) of the film. The effect of the starting population on the outcome of the simulation's discoveries is compared to the actual discoveries in order to define a most probable starting population.
A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.
Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita
Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. 2009 Wiley-Liss, Inc.
Effect of precipitation bias correction on water budget calculation in Upper Yellow River, China
International Nuclear Information System (INIS)
Ye Baisheng; Yang Daqing; Ma Lijuan
This study quantifies the effect of precipitation bias corrections on basin water balance calculations for the Yellow River Source region (YRS). We analyse long-term (1959–2001) monthly and yearly data of precipitation, runoff, and ERA-40 water budget variables and define a water balance regime. Basin precipitation, evapotranspiration and runoff are high in summer and low in winter. The basin water storage change is positive in summer and negative in winter. Monthly precipitation bias corrections, ranging from 2 to 16 mm, do not significantly alter the pattern of the seasonal water budget. The annual bias correction of precipitation is about 98 mm (19%); this increase leads to the same amount of evapotranspiration increase, since yearly runoff remains unchanged and the long-term storage change is assumed to be zero. Annual runoff and evapotranspiration coefficients change, due to precipitation bias corrections, from 0.33 and 0.67 to 0.28 and 0.72, respectively. These changes will impact the parameterization and calibration of land surface and hydrological models. The bias corrections of precipitation data also improve the relationship between annual precipitation and runoff. (letter)
Addressing the mischaracterization of extreme rainfall in regional climate model simulations - A synoptic pattern based bias correction approach
Li, Jingwan; Sharma, Ashish; Evans, Jason; Johnson, Fiona
Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
Cannon, Alex J.
Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin
Short Tree, Long Tree, Right Tree, Wrong Tree: New Acquisition Bias Corrections for Inferring SNP Phylogenies.
Leaché, Adam D; Banbury, Barbara L; Felsenstein, Joseph; de Oca, Adrián Nieto-Montes; Stamatakis, Alexandros
Single nucleotide polymorphisms (SNPs) are useful markers for phylogenetic studies owing in part to their ubiquity throughout the genome and ease of collection. Restriction site associated DNA sequencing (RADseq) methods are becoming increasingly popular for SNP data collection, but an assessment of the best practises for using these data in phylogenetics is lacking. We use computer simulations, and new double digest RADseq (ddRADseq) data for the lizard family Phrynosomatidae, to investigate the accuracy of RAD loci for phylogenetic inference. We compare the two primary ways RAD loci are used during phylogenetic analysis, including the analysis of full sequences (i.e., SNPs together with invariant sites), or the analysis of SNPs on their own after excluding invariant sites. We find that using full sequences rather than just SNPs is preferable from the perspectives of branch length and topological accuracy, but not of computational time. We introduce two new acquisition bias corrections for dealing with alignments composed exclusively of SNPs, a conditional likelihood method and a reconstituted DNA approach. The conditional likelihood method conditions on the presence of variable characters only (the number of invariant sites that are unsampled but known to exist is not considered), while the reconstituted DNA approach requires the user to specify the exact number of unsampled invariant sites prior to the analysis. Under simulation, branch length biases increase with the amount of missing data for both acquisition bias correction methods, but branch length accuracy is much improved in the reconstituted DNA approach compared to the conditional likelihood approach. Phylogenetic analyses of the empirical data using concatenation or a coalescent-based species tree approach provide strong support for many of the accepted relationships among phrynosomatid lizards, suggesting that RAD loci contain useful phylogenetic signal across a range of divergence times despite the
Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association
Directory of Open Access Journals (Sweden)
Kelsey E. Grinde
Full Text Available To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as "winner's curse." We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10−6 and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.
Performance evaluation and bias correction of DBS measurements for a 1290-MHz boundary layer profiler.
Liu, Zhao; Zheng, Chaorong; Wu, Yue
Recently, the government installed a boundary layer profiler (BLP), which is operated under the Doppler beam swinging mode, in a coastal area of China, to acquire useful wind field information in the atmospheric boundary layer for several purposes. And under strong wind conditions, the performance of the BLP is evaluated. It is found that, even though the quality controlled BLP data show good agreement with the balloon observations, a systematic bias can always be found for the BLP data. For the low wind velocities, the BLP data tend to overestimate the atmospheric wind. However, with the increment of wind velocity, the BLP data show a tendency of underestimation. In order to remove the effect of poor quality data on bias correction, the probability distribution function of the differences between the two instruments is discussed, and it is found that the t location scale distribution is the most suitable probability model when compared to other probability models. After the outliers with a large discrepancy, which are outside of 95% confidence interval of the t location scale distribution, are discarded, the systematic bias can be successfully corrected using a first-order polynomial correction function. The methodology of bias correction used in the study not only can be referred for the correction of other wind profiling radars, but also can lay a solid basis for further analysis of the wind profiles.
Performance evaluation and bias correction of DBS measurements for a 1290-MHz boundary layer profiler
Liu, Zhao; Zheng, Chaorong; Wu, Yue
Recently, the government installed a boundary layer profiler (BLP), which is operated under the Doppler beam swinging mode, in a coastal area of China, to acquire useful wind field information in the atmospheric boundary layer for several purposes. And under strong wind conditions, the performance of the BLP is evaluated. It is found that, even though the quality controlled BLP data show good agreement with the balloon observations, a systematic bias can always be found for the BLP data. For the low wind velocities, the BLP data tend to overestimate the atmospheric wind. However, with the increment of wind velocity, the BLP data show a tendency of underestimation. In order to remove the effect of poor quality data on bias correction, the probability distribution function of the differences between the two instruments is discussed, and it is found that the t location scale distribution is the most suitable probability model when compared to other probability models. After the outliers with a large discrepancy, which are outside of 95% confidence interval of the t location scale distribution, are discarded, the systematic bias can be successfully corrected using a first-order polynomial correction function. The methodology of bias correction used in the study not only can be referred for the correction of other wind profiling radars, but also can lay a solid basis for further analysis of the wind profiles.
A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.
Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C
Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.
Retrospective correction of bias in diffusion tensor imaging arising from coil combination mode.
Sakaie, Ken; Lowe, Mark
To quantify and retrospectively correct for systematic differences in diffusion tensor imaging (DTI) measurements due to differences in coil combination mode. Multi-channel coils are now standard among MRI systems. There are several options for combining signal from multiple coils during image reconstruction, including sum-of-squares (SOS) and adaptive combine (AC). This contribution examines the bias between SOS- and AC-derived measures of tissue microstructure and a strategy for limiting that bias. Five healthy subjects were scanned under an institutional review board-approved protocol. Each set of raw image data was reconstructed twice-once with SOS and once with AC. The diffusion tensor was calculated from SOS- and AC-derived data by two algorithms-standard log-linear least squares and an approach that accounts for the impact of coil combination on signal statistics. Systematic differences between SOS and AC in terms of tissue microstructure (axial diffusivity, radial diffusivity, mean diffusivity and fractional anisotropy) were evaluated on a voxel-by-voxel basis. SOS-based tissue microstructure values are systematically lower than AC-based measures throughout the brain in each subject when using the standard tensor calculation method. The difference between SOS and AC can be virtually eliminated by taking into account the signal statistics associated with coil combination. The impact of coil combination mode on diffusion tensor-based measures of tissue microstructure is statistically significant but can be corrected retrospectively. The ability to do so is expected to facilitate pooling of data among imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.
Methods of Reducing Bias in Combined Thermal/Epithermal Neutron (CTEN) Assays of Heterogeneous Waste
Energy Technology Data Exchange (ETDEWEB)
Estep, R.J.; Melton, S.; Miko, D.
We examined the effectiveness of two different methods for correcting CTEN passive and active assays for bias due to variations in the source position in different drum types. Both use the same drum-averaged correction determined from a neural network trained to active flux monitor ratios as a starting point. One method then uses a neural network to obtain a spatial correction factor sensitive to the source location. The other method uses emission tomography. Both methods were found to give significantly improved assay accuracy over the drum-averaged correction, although more study is needed to determine which method works better.
Methods of Reducing Bias in Combined Thermal/Epithermal Neutron (CTEN) Assays of Heterogeneous Waste
International Nuclear Information System (INIS)
Estep, R.J.; Melton, S.; Miko, D.
We examined the effectiveness of two different methods for correcting CTEN passive and active assays for bias due to variations in the source position in different drum types. Both use the same drum-averaged correction determined from a neural network trained to active flux monitor ratios as a starting point. One method then uses a neural network to obtain a spatial correction factor sensitive to the source location. The other method uses emission tomography. Both methods were found to give significantly improved assay accuracy over the drum-averaged correction, although more study is needed to determine which method works better
Timing group delay and differential code bias corrections for BeiDou positioning
Guo, Fei; Zhang, Xiaohong; Wang, Jinling
This article first clearly figures out the relationship between parameters of timing group delay (TGD) and differential code bias (DCB) for BDS, and demonstrates the equivalence of TGD and DCB correction models combining theory with practice. The TGD/DCB correction models have been extended to various occasions for BDS positioning, and such models have been evaluated by real triple-frequency datasets. To test the effectiveness of broadcast TGDs in the navigation message and DCBs provided by the Multi-GNSS Experiment (MGEX), both standard point positioning (SPP) and precise point positioning (PPP) tests are carried out for BDS signals with different schemes. Furthermore, the influence of differential code biases on BDS positioning estimates such as coordinates, receiver clock biases, tropospheric delays and carrier phase ambiguities is investigated comprehensively. Comparative analysis show that the unmodeled differential code biases degrade the performance of BDS SPP by a factor of two or more, whereas the estimates of PPP are subject to varying degrees of influences. For SPP, the accuracy of dual-frequency combinations is slightly worse than that of single-frequency, and they are much more sensitive to the differential code biases, particularly for the B2B3 combination. For PPP, the uncorrected differential code biases are mostly absorbed into the receiver clock bias and carrier phase ambiguities and thus resulting in a much longer convergence time. Even though the influence of the differential code biases could be mitigated over time and comparable positioning accuracy could be achieved after convergence, it is suggested to properly handle with the differential code biases since it is vital for PPP convergence and integer ambiguity resolution.
Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems
Directory of Open Access Journals (Sweden)
Jianhu Zhao
Full Text Available Airborne LiDAR bathymetry (ALB is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model.
Joint deformable liver registration and bias field correction for MR-guided HDR brachytherapy.
Rak, Marko; König, Tim; Tönnies, Klaus D; Walke, Mathias; Ricke, Jens; Wybranski, Christian
In interstitial high-dose rate brachytherapy, liver cancer is treated by internal radiation, requiring percutaneous placement of applicators within or close to the tumor. To maximize utility, the optimal applicator configuration is pre-planned on magnetic resonance images. The pre-planned configuration is then implemented via a magnetic resonance-guided intervention. Mapping the pre-planning information onto interventional data would reduce the radiologist's cognitive load during the intervention and could possibly minimize discrepancies between optimally pre-planned and actually placed applicators. We propose a fast and robust two-step registration framework suitable for interventional settings: first, we utilize a multi-resolution rigid registration to correct for differences in patient positioning (rotation and translation). Second, we employ a novel iterative approach alternating between bias field correction and Markov random field deformable registration in a multi-resolution framework to compensate for non-rigid movements of the liver, the tumors and the organs at risk. In contrast to existing pre-correction methods, our multi-resolution scheme can recover bias field artifacts of different extents at marginal computational costs. We compared our approach to deformable registration via B-splines, demons and the SyN method on 22 registration tasks from eleven patients. Results showed that our approach is more accurate than the contenders for liver as well as for tumor tissues. We yield average liver volume overlaps of 94.0 ± 2.7% and average surface-to-surface distances of 2.02 ± 0.87 mm and 3.55 ± 2.19 mm for liver and tumor tissue, respectively. The reported distances are close to (or even below) the slice spacing (2.5 - 3.0 mm) of our data. Our approach is also the fastest, taking 35.8 ± 12.8 s per task. The presented approach is sufficiently accurate to map information available from brachytherapy pre-planning onto interventional data. It
A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models
Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.
Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.
Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR.
Ouyang, Jinsong; Chun, Se Young; Petibon, Yoann; Bonab, Ali A; Alpert, Nathaniel; Fakhri, Georges El
This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.
A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction.
Tang, Jian; Jiang, Xiaoliang
Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.
Assessing climate change impacts on the rape stem weevil, Ceutorhynchus napi Gyll., based on bias- and non-bias-corrected regional climate change projections
Junk, J.; Ulber, B.; Vidal, S.; Eickermann, M.
Agricultural production is directly affected by projected increases in air temperature and changes in precipitation. A multi-model ensemble of regional climate change projections indicated shifts towards higher air temperatures and changing precipitation patterns during the summer and winter seasons up to the year 2100 for the region of Goettingen (Lower Saxony, Germany). A second major controlling factor of the agricultural production is the infestation level by pests. Based on long-term field surveys and meteorological observations, a calibration of an existing model describing the migration of the pest insect Ceutorhynchus napi was possible. To assess the impacts of climate on pests under projected changing environmental conditions, we combined the results of regional climate models with the phenological model to describe the crop invasion of this species. In order to reduce systematic differences between the output of the regional climate models and observational data sets, two different bias correction methods were applied: a linear correction for air temperature and a quantile mapping approach for precipitation. Only the results derived from the bias-corrected output of the regional climate models showed satisfying results. An earlier onset, as well as a prolongation of the possible time window for the immigration of Ceutorhynchus napi, was projected by the majority of the ensemble members.
Assessing climate change impacts on the rape stem weevil, Ceutorhynchus napi Gyll., based on bias- and non-bias-corrected regional climate change projections.
Junk, J; Ulber, B; Vidal, S; Eickermann, M
Agricultural production is directly affected by projected increases in air temperature and changes in precipitation. A multi-model ensemble of regional climate change projections indicated shifts towards higher air temperatures and changing precipitation patterns during the summer and winter seasons up to the year 2100 for the region of Goettingen (Lower Saxony, Germany). A second major controlling factor of the agricultural production is the infestation level by pests. Based on long-term field surveys and meteorological observations, a calibration of an existing model describing the migration of the pest insect Ceutorhynchus napi was possible. To assess the impacts of climate on pests under projected changing environmental conditions, we combined the results of regional climate models with the phenological model to describe the crop invasion of this species. In order to reduce systematic differences between the output of the regional climate models and observational data sets, two different bias correction methods were applied: a linear correction for air temperature and a quantile mapping approach for precipitation. Only the results derived from the bias-corrected output of the regional climate models showed satisfying results. An earlier onset, as well as a prolongation of the possible time window for the immigration of Ceutorhynchus napi, was projected by the majority of the ensemble members.
The Systematic Bias of Ingestible Core Temperature Sensors Requires a Correction by Linear Regression.
Hunt, Andrew P; Bach, Aaron J E; Borg, David N; Costello, Joseph T; Stewart, Ian B
An accurate measure of core body temperature is critical for monitoring individuals, groups and teams undertaking physical activity in situations of high heat stress or prolonged cold exposure. This study examined the range in systematic bias of ingestible temperature sensors compared to a certified and traceable reference thermometer. A total of 119 ingestible temperature sensors were immersed in a circulated water bath at five water temperatures (TEMP A: 35.12 ± 0.60°C, TEMP B: 37.33 ± 0.56°C, TEMP C: 39.48 ± 0.73°C, TEMP D: 41.58 ± 0.97°C, and TEMP E: 43.47 ± 1.07°C) along with a certified traceable reference thermometer. Thirteen sensors (10.9%) demonstrated a systematic bias > ±0.1°C, of which 4 (3.3%) were > ± 0.5°C. Limits of agreement (95%) indicated that systematic bias would likely fall in the range of -0.14 to 0.26°C, highlighting that it is possible for temperatures measured between sensors to differ by more than 0.4°C. The proportion of sensors with systematic bias > ±0.1°C (10.9%) confirms that ingestible temperature sensors require correction to ensure their accuracy. An individualized linear correction achieved a mean systematic bias of 0.00°C, and limits of agreement (95%) to 0.00-0.00°C, with 100% of sensors achieving ±0.1°C accuracy. Alternatively, a generalized linear function (Corrected Temperature (°C) = 1.00375 × Sensor Temperature (°C) - 0.205549), produced as the average slope and intercept of a sub-set of 51 sensors and excluding sensors with accuracy outside ±0.5°C, reduced the systematic bias to Correction of sensor temperature to a reference thermometer by linear function eliminates this systematic bias (individualized functions) or ensures systematic bias is within ±0.1°C in 98% of the sensors (generalized function).
The Systematic Bias of Ingestible Core Temperature Sensors Requires a Correction by Linear Regression
Directory of Open Access Journals (Sweden)
Andrew P. Hunt
Full Text Available An accurate measure of core body temperature is critical for monitoring individuals, groups and teams undertaking physical activity in situations of high heat stress or prolonged cold exposure. This study examined the range in systematic bias of ingestible temperature sensors compared to a certified and traceable reference thermometer. A total of 119 ingestible temperature sensors were immersed in a circulated water bath at five water temperatures (TEMP A: 35.12 ± 0.60°C, TEMP B: 37.33 ± 0.56°C, TEMP C: 39.48 ± 0.73°C, TEMP D: 41.58 ± 0.97°C, and TEMP E: 43.47 ± 1.07°C along with a certified traceable reference thermometer. Thirteen sensors (10.9% demonstrated a systematic bias > ±0.1°C, of which 4 (3.3% were > ± 0.5°C. Limits of agreement (95% indicated that systematic bias would likely fall in the range of −0.14 to 0.26°C, highlighting that it is possible for temperatures measured between sensors to differ by more than 0.4°C. The proportion of sensors with systematic bias > ±0.1°C (10.9% confirms that ingestible temperature sensors require correction to ensure their accuracy. An individualized linear correction achieved a mean systematic bias of 0.00°C, and limits of agreement (95% to 0.00–0.00°C, with 100% of sensors achieving ±0.1°C accuracy. Alternatively, a generalized linear function (Corrected Temperature (°C = 1.00375 × Sensor Temperature (°C − 0.205549, produced as the average slope and intercept of a sub-set of 51 sensors and excluding sensors with accuracy outside ±0.5°C, reduced the systematic bias to < ±0.1°C in 98.4% of the remaining sensors (n = 64. In conclusion, these data show that using an uncalibrated ingestible temperature sensor may provide inaccurate data that still appears to be statistically, physiologically, and clinically meaningful. Correction of sensor temperature to a reference thermometer by linear function eliminates this systematic bias (individualized functions or ensures
Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields
Directory of Open Access Journals (Sweden)
S. Vogl
Full Text Available This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values.
In this study copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. After appropriate time series transformation to generate "iid" variates, only the positive pairs (radar >0, gauge >0 of the residuals are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches, namely Maximum Theta and Multiple Theta, are presented. They finally allow for generating precipitation fields that mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar fields, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (ordinary kriging and are validated with available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN. The copula-based approach performs similarly well as indicated by different validation measures and successfully corrects for errors in the radar precipitation.
A hybrid numerical method for orbit correction
International Nuclear Information System (INIS)
White, G.; Himel, T.; Shoaee, H.
The authors describe a simple hybrid numerical method for beam orbit correction in particle accelerators. The method overcomes both degeneracy in the linear system being solved and respects boundaries on the solution. It uses the Singular Value Decomposition (SVD) to find and remove the null-space in the system, followed by a bounded Linear Least Squares analysis of the remaining recast problem. It was developed for correcting orbit and dispersion in the B-factory rings
Bias correction of risk estimates in vaccine safety studies with rare adverse events using a self-controlled case series design.
Zeng, Chan; Newcomer, Sophia R; Glanz, Jason M; Shoup, Jo Ann; Daley, Matthew F; Hambidge, Simon J; Xu, Stanley
The self-controlled case series (SCCS) method is often used to examine the temporal association between vaccination and adverse events using only data from patients who experienced such events. Conditional Poisson regression models are used to estimate incidence rate ratios, and these models perform well with large or medium-sized case samples. However, in some vaccine safety studies, the adverse events studied are rare and the maximum likelihood estimates may be biased. Several bias correction methods have been examined in case-control studies using conditional logistic regression, but none of these methods have been evaluated in studies using the SCCS design. In this study, we used simulations to evaluate 2 bias correction approaches-the Firth penalized maximum likelihood method and Cordeiro and McCullagh's bias reduction after maximum likelihood estimation-with small sample sizes in studies using the SCCS design. The simulations showed that the bias under the SCCS design with a small number of cases can be large and is also sensitive to a short risk period. The Firth correction method provides finite and less biased estimates than the maximum likelihood method and Cordeiro and McCullagh's method. However, limitations still exist when the risk period in the SCCS design is short relative to the entire observation period.
Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX.
Oh, Eric J; Shepherd, Bryan E; Lumley, Thomas; Shaw, Pamela A
For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 common regression models, the Cox and Weibull models, in the setting of measurement error in the failure time outcome. We introduce an extension of the SIMEX method to correct for bias in hazard ratio estimates from the Cox model and discuss other analysis options to address measurement error in the response. A formula to estimate the bias induced into the hazard ratio by classical measurement error in the event time for a log-linear survival model is presented. Detailed numerical studies are presented to examine the performance of the proposed SIMEX method under varying levels and parametric forms of the error in the outcome. We further illustrate the method with observational data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic. Copyright © 2017 John Wiley & Sons, Ltd.
p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results.
Simonsohn, Uri; Nelson, Leif D; Simmons, Joseph P
Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the "choice overload" literature. © The Author(s) 2014.
Improving RNA-Seq expression estimates by correcting for fragment bias
The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies. PMID:21410973
Is the Pearson r[squared] Biased, and if So, What Is the Best Correction Formula?
Wang, Zhongmiao; Thompson, Bruce
In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…
Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.
Young Kim, Eun; Johnson, Hans J
A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.
Bias correction of nutritional status estimates when reported age is used for calculating WHO indicators in children under five years of age
Directory of Open Access Journals (Sweden)
Amado D Quezada
Full Text Available Objective.To assess the performance of a simple correction method for nutritional status estimates in children under five years of age when exact age is not available from the data. Materials and methods. The proposed method was ba- sed on the assumption of symmetry of age distributions within a given month of age and validated in a large population-based survey sample of Mexican preschool children. Results. The main distributional assumption was consistent with the data. All prevalence estimates derived from the correction method showed no statistically significant bias. In contrast, failing to correct attained age resulted in an underestimation of stunting in general and an overestimation of overweight or obesity among the youngest. Conclusions. The proposed method performed remarkably well in terms of bias correction of estimates and could be easily applied in situations in which either birth or interview dates are not available from the data.
Uncertainty estimation with bias-correction for flow series based on rating curve
Shao, Quanxi; Lerat, Julien; Podger, Geoff; Dutta, Dushmanta
Streamflow discharge constitutes one of the fundamental data required to perform water balance studies and develop hydrological models. A rating curve, designed based on a series of concurrent stage and discharge measurements at a gauging location, provides a way to generate complete discharge time series with a reasonable quality if sufficient measurement points are available. However, the associated uncertainty is frequently not available even though it has a significant impact on hydrological modelling. In this paper, we identify the discrepancy of the hydrographers' rating curves used to derive the historical discharge data series and proposed a modification by bias correction which is also in the form of power function as the traditional rating curve. In order to obtain the uncertainty estimation, we propose a further both-side Box-Cox transformation to stabilize the regression residuals as close to the normal distribution as possible, so that a proper uncertainty can be attached for the whole discharge series in the ensemble generation. We demonstrate the proposed method by applying it to the gauging stations in the Flinders and Gilbert rivers in north-west Queensland, Australia.
Stochastic bias-correction of daily rainfall scenarios for hydrological applications
Directory of Open Access Journals (Sweden)
I. Portoghese
Full Text Available The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge.
In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.
Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection.
Carnegie, Nicole Bohme
The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617–4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed cases. This method has been implemented by public health departments across the United States and is behind the new national incidence estimates, which are about 40 per cent higher than previous estimates. We show that the delta method approximation given for the variance of the estimator is incomplete, leading to an inflated variance estimate. This contributes to the generation of overly conservative confidence intervals, potentially obscuring important differences between populations. We demonstrate via simulation that an innovative model-based bootstrap method using the specified model for the infection and surveillance process improves confidence interval coverage and adjusts for the bias in the point estimate. Confidence interval coverage is about 94–97 per cent after correction, compared with 96–99 per cent before. The simulated bias in the estimate of incidence ranges from −6.3 to +14.6 per cent under the original model but is consistently under 1 per cent after correction by the model-based bootstrap. In an application to data from King County, Washington in 2007 we observe correction of 7.2 per cent relative bias in the incidence estimate and a 66 per cent reduction in the width of the 95 per cent confidence interval using this method. We provide open-source software to implement the method that can also be extended for alternate models.
Bias correction of nutritional status estimates when reported age is used for calculating WHO indicators in children under five years of age.
Quezada, Amado D; García-Guerra, Armando; Escobar, Leticia
To assess the performance of a simple correction method for nutritional status estimates in children under five years of age when exact age is not available from the data. The proposed method was based on the assumption of symmetry of age distributions within a given month of age and validated in a large population-based survey sample of Mexican preschool children. The main distributional assumption was consistent with the data. All prevalence estimates derived from the correction method showed no statistically significant bias. In contrast, failing to correct attained age resulted in an underestimation of stunting in general and an overestimation of overweight or obesity among the youngest. The proposed method performed remarkably well in terms of bias correction of estimates and could be easily applied in situations in which either birth or interview dates are not available from the data.
Direct estimation and correction of bias from temporally variable non-stationary noise in a channelized Hotelling model observer.
Fetterly, Kenneth A; Favazza, Christopher P
Channelized Hotelling model observer (CHO) methods were developed to assess performance of an x-ray angiography system. The analytical methods included correction for known bias error due to finite sampling. Detectability indices ([Formula: see text]) corresponding to disk-shaped objects with diameters in the range 0.5-4 mm were calculated. Application of the CHO for variable detector target dose (DTD) in the range 6-240 nGy frame(-1) resulted in [Formula: see text] estimates which were as much as 2.9× greater than expected of a quantum limited system. Over-estimation of [Formula: see text] was presumed to be a result of bias error due to temporally variable non-stationary noise. Statistical theory which allows for independent contributions of 'signal' from a test object (o) and temporally variable non-stationary noise (ns) was developed. The theory demonstrates that the biased [Formula: see text] is the sum of the detectability indices associated with the test object [Formula: see text] and non-stationary noise ([Formula: see text]). Given the nature of the imaging system and the experimental methods, [Formula: see text] cannot be directly determined independent of [Formula: see text]. However, methods to estimate [Formula: see text] independent of [Formula: see text] were developed. In accordance with the theory, [Formula: see text] was subtracted from experimental estimates of [Formula: see text], providing an unbiased estimate of [Formula: see text]. Estimates of [Formula: see text] exhibited trends consistent with expectations of an angiography system that is quantum limited for high DTD and compromised by detector electronic readout noise for low DTD conditions. Results suggest that these methods provide [Formula: see text] estimates which are accurate and precise for [Formula: see text]. Further, results demonstrated that the source of bias was detector electronic readout noise. In summary, this work presents theory and methods to test for the
Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.
Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui
Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.
Bias correction in species distribution models: pooling survey and collection data for multiple species.
Fithian, William; Elith, Jane; Hastie, Trevor; Keith, David A
Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant.We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data.We evaluate our model's performance on data for 36 eucalypt species in south-eastern Australia. We find that presence-only records exhibit a strong sampling bias towards the coast and towards Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarceIf we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range.
A New Class of Scaling Correction Methods
International Nuclear Information System (INIS)
Mei Li-Jie; Wu Xin; Liu Fu-Yao
When conventional integrators like Runge—Kutta-type algorithms are used, numerical errors can make an orbit deviate from a hypersurface determined by many constraints, which leads to unreliable numerical solutions. Scaling correction methods are a powerful tool to avoid this. We focus on their applications, and also develop a family of new velocity multiple scaling correction methods where scale factors only act on the related components of the integrated momenta. They can preserve exactly some first integrals of motion in discrete or continuous dynamical systems, so that rapid growth of roundoff or truncation errors is suppressed significantly. (general)
[Retrospective analysis of Mexican National Addictions Survey, 2008. Bias identification and correction].
Romero-Martínez, Martín; Téllez-Rojo Solís, Martha María; Sandoval-Zárate, América Andrea; Zurita-Luna, Juan Manuel; Gutiérrez-Reyes, Juan Pablo
To determine the presence of bias on the estimation of the consumption sometime in life of alcohol, tobacco or illegal drugs and inhalable substances, and to propose a correction for this in the case it is present. Mexican National Addictions Surveys (NAS) 2002, 2008, and 2011 were analyzed to compare population estimations of consumption sometime in life of tobacco, alcohol or illegal drugs and inhalable substances. A couple of alternative approaches for bias correction were developed. Estimated national prevalences of consumption sometime in life of alcohol and tobacco in the NAS 2008 are not plausible. There was no evidence of bias on the consumption sometime in life of illegal drugs and inhalable substances. New estimations for tobacco and alcohol consumption sometime in life were made, which resulted in plausible values when compared to other data available. Future analyses regarding tobacco and alcohol using NAS 2008 data will have to rely on these newly generated data weights, that are able to reproduce the new (plausible) estimations.
Another method of dead time correction
International Nuclear Information System (INIS)
Sabol, J.
A new method of the correction of counting losses caused by a non-extended dead time of pulse detection systems is presented. The approach is based on the distribution of time intervals between pulses at the output of the system. The method was verified both experimentally and by using the Monte Carlo simulations. The results show that the suggested technique is more reliable and accurate than other methods based on a separate measurement of the dead time. (author) 5 refs
Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction
Directory of Open Access Journals (Sweden)
C. L. Keppenne
Full Text Available To compensate for a poorly known geoid, satellite altimeter data is usually analyzed in terms of anomalies from the time mean record. When such anomalies are assimilated into an ocean model, the bias between the climatologies of the model and data is problematic. An ensemble Kalman filter (EnKF is modified to account for the presence of a forecast-model bias and applied to the assimilation of TOPEX/Poseidon (T/P altimeter data. The online bias correction (OBC algorithm uses the same ensemble of model state vectors to estimate biased-error and unbiased-error covariance matrices. Covariance localization is used but the bias covariances have different localization scales from the unbiased-error covariances, thereby accounting for the fact that the bias in a global ocean model could have much larger spatial scales than the random error.The method is applied to a 27-layer version of the Poseidon global ocean general circulation model with about 30-million state variables. Experiments in which T/P altimeter anomalies are assimilated show that the OBC reduces the RMS observation minus forecast difference for sea-surface height (SSH over a similar EnKF run in which OBC is not used. Independent in situ temperature observations show that the temperature field is also improved. When the T/P data and in situ temperature data are assimilated in the same run and the configuration of the ensemble at the end of the run is used to initialize the ocean component of the GMAO coupled forecast model, seasonal SSH hindcasts made with the coupled model are generally better than those initialized with optimal interpolation of temperature observations without altimeter data. The analysis of the corresponding sea-surface temperature hindcasts is not as conclusive.
Off-Angle Iris Correction Methods
Energy Technology Data Exchange (ETDEWEB)
Santos-Villalobos, Hector J [ORNL; Thompson, Joseph T [ORNL; Karakaya, Mahmut [ORNL; Boehnen, Chris Bensing [ORNL
In many real world iris recognition systems obtaining consistent frontal images is problematic do to inexperienced or uncooperative users, untrained operators, or distracting environments. As a result many collected images are unusable by modern iris matchers. In this chapter we present four methods for correcting off-angle iris images to appear frontal which makes them compatible with existing iris matchers. The methods include an affine correction, a retraced model of the human eye, measured displacements, and a genetic algorithm optimized correction. The affine correction represents a simple way to create an iris image that appears frontal but it does not account for refractive distortions of the cornea. The other method account for refraction. The retraced model simulates the optical properties of the cornea. The other two methods are data driven. The first uses optical flow to measure the displacements of the iris texture when compared to frontal images of the same subject. The second uses a genetic algorithm to learn a mapping that optimizes the Hamming Distance scores between off-angle and frontal images. In this paper we hypothesize that the biological model presented in our earlier work does not adequately account for all variations in eye anatomy and therefore the two data-driven approaches should yield better performance. Results are presented using the commercial VeriEye matcher that show that the genetic algorithm method clearly improves over prior work and makes iris recognition possible up to 50 degrees off-angle.
Iteration of ultrasound aberration correction methods
Maasoey, Svein-Erik; Angelsen, Bjoern; Varslot, Trond
Aberration in ultrasound medical imaging is usually modeled by time-delay and amplitude variations concentrated on the transmitting/receiving array. This filter process is here denoted a TDA filter. The TDA filter is an approximation to the physical aberration process, which occurs over an extended part of the human body wall. Estimation of the TDA filter, and performing correction on transmit and receive, has proven difficult. It has yet to be shown that this method works adequately for severe aberration. Estimation of the TDA filter can be iterated by retransmitting a corrected signal and re-estimate until a convergence criterion is fulfilled (adaptive imaging). Two methods for estimating time-delay and amplitude variations in receive signals from random scatterers have been developed. One method correlates each element signal with a reference signal. The other method use eigenvalue decomposition of the receive cross-spectrum matrix, based upon a receive energy-maximizing criterion. Simulations of iterating aberration correction with a TDA filter have been investigated to study its convergence properties. A weak and strong human-body wall model generated aberration. Both emulated the human abdominal wall. Results after iteration improve aberration correction substantially, and both estimation methods converge, even for the case of strong aberration.
Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement
Azorin-Molina, Cesar; Asin, Jesus; McVicar, Tim R.; Minola, Lorenzo; Lopez-Moreno, Juan I.; Vicente-Serrano, Sergio M.; Chen, Deliang
Recent studies on observed wind variability have revealed a decline (termed "stilling") of near-surface wind speed during the last 30-50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014-2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer.
A model-based correction for outcome reporting bias in meta-analysis.
Copas, John; Dwan, Kerry; Kirkham, Jamie; Williamson, Paula
It is often suspected (or known) that outcomes published in medical trials are selectively reported. A systematic review for a particular outcome of interest can only include studies where that outcome was reported and so may omit, for example, a study that has considered several outcome measures but only reports those giving significant results. Using the methodology of the Outcome Reporting Bias (ORB) in Trials study of (Kirkham and others, 2010. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. British Medical Journal 340, c365), we suggest a likelihood-based model for estimating the effect of ORB on confidence intervals and p-values in meta-analysis. Correcting for bias has the effect of moving estimated treatment effects toward the null and hence more cautious assessments of significance. The bias can be very substantial, sometimes sufficient to completely overturn previous claims of significance. We re-analyze two contrasting examples, and derive a simple fixed effects approximation that can be used to give an initial estimate of the effect of ORB in practice.
Reproducibility and day time bias correction of optoelectronic leg volumetry: a prospective cohort study
Directory of Open Access Journals (Sweden)
Baumgartner Iris
Full Text Available Abstract Background Leg edema is a common manifestation of various underlying pathologies. Reliable measurement tools are required to quantify edema and monitor therapeutic interventions. Aim of the present work was to investigate the reproducibility of optoelectronic leg volumetry over 3 weeks' time period and to eliminate daytime related within-individual variability. Methods Optoelectronic leg volumetry was performed in 63 hairdressers (mean age 45 ± 16 years, 85.7% female in standing position twice within a minute for each leg and repeated after 3 weeks. Both lower leg (legBD and whole limb (limbBF volumetry were analysed. Reproducibility was expressed as analytical and within-individual coefficients of variance (CVA, CVW, and as intra-class correlation coefficients (ICC. Results A total of 492 leg volume measurements were analysed. Both legBD and limbBF volumetry were highly reproducible with CVA of 0.5% and 0.7%, respectively. Within-individual reproducibility of legBD and limbBF volumetry over a three weeks' period was high (CVW 1.3% for both; ICC 0.99 for both. At both visits, the second measurement revealed a significantly higher volume compared to the first measurement with a mean increase of 7.3 ml ± 14.1 (0.33% ± 0.58% for legBD and 30.1 ml ± 48.5 ml (0.52% ± 0.79% for limbBF volume. A significant linear correlation between absolute and relative leg volume differences and the difference of exact day time of measurement between the two study visits was found (P W. Conclusions Leg volume changes can be reliably assessed by optoelectronic leg volumetry at a single time point and over a 3 weeks' time period. However, volumetry results are biased by orthostatic and daytime-related volume changes. The bias for day-time related volume changes can be minimized by a time-correction formula.
Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.
Ning, Jing; Chen, Yong; Piao, Jin
Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: email@example.com.
Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias
International Nuclear Information System (INIS)
Yoo, Seung-Hoon; Lim, Hea-Jin; Kwak, Seung-Jun
Over the last twenty years, the consumption of natural gas in Korea has increased dramatically. This increase has mainly resulted from the rise of consumption in the residential sector. The main objective of the study is to estimate households' demand function for natural gas by applying a sample selection model using data from a survey of households in Seoul. The results show that there exists a selection bias in the sample and that failure to correct for sample selection bias distorts the mean estimate, of the demand for natural gas, downward by 48.1%. In addition, according to the estimation results, the size of the house, the dummy variable for dwelling in an apartment, the dummy variable for having a bed in an inner room, and the household's income all have positive relationships with the demand for natural gas. On the other hand, the size of the family and the price of gas negatively contribute to the demand for natural gas. (author)
Efficient orbit integration by manifold correction methods.
Fukushima, Toshio
Triggered by a desire to investigate, numerically, the planetary precession through a long-term numerical integration of the solar system, we developed a new formulation of numerical integration of orbital motion named manifold correct on methods. The main trick is to rigorously retain the consistency of physical relations, such as the orbital energy, the orbital angular momentum, or the Laplace integral, of a binary subsystem. This maintenance is done by applying a correction to the integrated variables at each integration step. Typical methods of correction are certain geometric transformations, such as spatial scaling and spatial rotation, which are commonly used in the comparison of reference frames, or mathematically reasonable operations, such as modularization of angle variables into the standard domain [-pi, pi). The form of the manifold correction methods finally evolved are the orbital longitude methods, which enable us to conduct an extremely precise integration of orbital motions. In unperturbed orbits, the integration errors are suppressed at the machine epsilon level for an indefinitely long period. In perturbed cases, on the other hand, the errors initially grow in proportion to the square root of time and then increase more rapidly, the onset of which depends on the type and magnitude of the perturbations. This feature is also realized for highly eccentric orbits by applying the same idea as used in KS-regularization. In particular, the introduction of time elements greatly enhances the performance of numerical integration of KS-regularized orbits, whether the scaling is applied or not.
TRMM-3B43 Bias Correction over the High Elevations of the Contiguous United States
Hashemi, H.; Nordin, K. M.; Lakshmi, V.; Knight, R. J.
Precipitation can be quantified using a rain gauge network, or a remotely sensed precipitation product. Ultimately, the choice of dataset depends on the particular application, the catchment size, climate and the time period of study. In a region with a long record and a dense rain gauge network, the elevation-modified ground-based precipitation product, PRISM, has been found to work well. However, in poorly gauged regions the use of remotely sensed precipitation products is an absolute necessity. The Tropical Rainfall Measuring Mission (TRMM) has provided valuable precipitation datasets for hydrometeorological studies over the past two decades (1998-2015). One concern regarding the usage of TRMM data is the accuracy of the precipitation estimates, when compared to those obtained using PRISM. The reason for this concern is that TRMM and PRISM do not always agree and, typically, TRMM underestimates PRISM over the mountainous regions of the United States. In this study, we develop a correction function to improve the accuracy of the TRMM monthly product (TRMM-3B43) by estimating and removing the bias in the satellite data using the ground-based precipitation product, PRISM. We observe a strong relationship between the bias and land surface elevation; TRMM-3B43 tends to underestimate the PRISM product at altitudes greater than 1500 m above mean sea level (m.amsl) in the contiguous United States. A relationship is developed between TRMM-PRISM bias and elevation. The correction function is used to adjust the TRMM monthly precipitation using PRISM and elevation data. The model is calibrated using 25% of the available time period and the remaining 75% of the time period is used for validation. The corrected TRMM-3B43 product is verified for the high elevations over the contiguous United States and two local regions in the mountainous areas of the western United States. The results show a significant improvement in the accuracy of the TRMM product in the high elevations of
Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India
Beria, H.; Nanda, T., Sr.; Chatterjee, C.
High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.
Bias Correction and Random Error Characterization for the Assimilation of HRDI Line-of-Sight Wind Measurements
Tangborn, Andrew; Menard, Richard; Ortland, David; Einaudi, Franco (Technical Monitor)
A new approach to the analysis of systematic and random observation errors is presented in which the error statistics are obtained using forecast data rather than observations from a different instrument type. The analysis is carried out at an intermediate retrieval level, instead of the more typical state variable space. This method is carried out on measurements made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). HRDI, a limb sounder, is the only satellite instrument measuring winds in the stratosphere, and the only instrument of any kind making global wind measurements in the upper atmosphere. HRDI measures doppler shifts in the two different O2 absorption bands (alpha and B) and the retrieved products are tangent point Line-of-Sight wind component (level 2 retrieval) and UV winds (level 3 retrieval). This analysis is carried out on a level 1.9 retrieval, in which the contributions from different points along the line-of-sight have not been removed. Biases are calculated from O-F (observed minus forecast) LOS wind components and are separated into a measurement parameter space consisting of 16 different values. The bias dependence on these parameters (plus an altitude dependence) is used to create a bias correction scheme carried out on the level 1.9 retrieval. The random error component is analyzed by separating the gamma and B band observations and locating observation pairs where both bands are very nearly looking at the same location at the same time. It is shown that the two observation streams are uncorrelated and that this allows the forecast error variance to be estimated. The bias correction is found to cut the effective observation error variance in half.
Methods of correcting Anger camera deadtime losses
International Nuclear Information System (INIS)
Sorenson, J.A.
Three different methods of correcting for Anger camera deadtime loss were investigated. These included analytic methods (mathematical modeling), the marker-source method, and a new method based on counting ''pileup'' events appearing in a pulseheight analyzer window positioned above the photopeak of interest. The studies were done with /sup 99m/Tc on a Searle Radiographics camera with a measured deadtime of about 6 μsec. Analytic methods were found to be unreliable because of unpredictable changes in deadtime with changes in radiation scattering conditions. Both the marker-source method and the pileup-counting method were found to be accurate to within a few percent for true counting rates of up to about 200 K cps, with the pileup-counting method giving better results. This finding applied to sources at depths ranging up to 10 cm of pressed wood. The relative merits of the two methods are discussed
A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection
Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.
Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.
Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions
Directory of Open Access Journals (Sweden)
Asma Foughali
Full Text Available This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2 in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT from the European program ENSEMBLES, forced by two global climate models (GCM: ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to
Correcting for non-response bias in contingent valuation surveys concerning environmental non-market goods
DEFF Research Database (Denmark)
Bonnichsen, Ole; Olsen, Søren Bøye
Data collection for economic valuation by using Internet surveys and pre-recruited Internet panels can be associated with severe disadvantages. Problems concerning sample coverage and sample representativeness can be expected. Representation errors may occur since people can choose whether....... This paper analyses a sample used for an Internet contingent valuation method survey eliciting preferences for improvements in water quality of a river. We find that some variables that affect the survey participation decision also affect willingness-to-pay, consequently biasing our welfare estimates. We...... show how adjusting willingness-to-pay for this bias can be accomplished by using a grouped data model incorporating a correlation parameter to account for selection....
Correcting the bias of empirical frequency parameter estimators in codon models.
Directory of Open Access Journals (Sweden)
Sergei Kosakovsky Pond
Full Text Available Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.
Reduction of determinate errors in mass bias-corrected isotope ratios measured using a multi-collector plasma mass spectrometer
International Nuclear Information System (INIS)
Doherty, W.
A nebulizer-centric instrument response function model of the plasma mass spectrometer was combined with a signal drift model, and the result was used to identify the causes of the non-spectroscopic determinate errors remaining in mass bias-corrected Pb isotope ratios (Tl as internal standard) measured using a multi-collector plasma mass spectrometer. Model calculations, confirmed by measurement, show that the detectable time-dependent errors are a result of the combined effect of signal drift and differences in the coordinates of the Pb and Tl response function maxima (horizontal offset effect). If there are no horizontal offsets, then the mass bias-corrected isotope ratios are approximately constant in time. In the absence of signal drift, the response surface curvature and horizontal offset effects are responsible for proportional errors in the mass bias-corrected isotope ratios. The proportional errors will be different for different analyte isotope ratios and different at every instrument operating point. Consequently, mass bias coefficients calculated using different isotope ratios are not necessarily equal. The error analysis based on the combined model provides strong justification for recommending a three step correction procedure (mass bias correction, drift correction and a proportional error correction, in that order) for isotope ratio measurements using a multi-collector plasma mass spectrometer
Dead time corrections using the backward extrapolation method
Energy Technology Data Exchange (ETDEWEB)
Gilad, E., E-mail: firstname.lastname@example.com [The Unit of Nuclear Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel); Dubi, C. [Department of Physics, Nuclear Research Center NEGEV (NRCN), Beer-Sheva 84190 (Israel); Geslot, B.; Blaise, P. [DEN/CAD/DER/SPEx/LPE, CEA Cadarache, Saint-Paul-les-Durance 13108 (France); Kolin, A. [Department of Physics, Nuclear Research Center NEGEV (NRCN), Beer-Sheva 84190 (Israel)
Dead time losses in neutron detection, caused by both the detector and the electronics dead time, is a highly nonlinear effect, known to create high biasing in physical experiments as the power grows over a certain threshold, up to total saturation of the detector system. Analytic modeling of the dead time losses is a highly complicated task due to the different nature of the dead time in the different components of the monitoring system (e.g., paralyzing vs. non paralyzing), and the stochastic nature of the fission chains. In the present study, a new technique is introduced for dead time corrections on the sampled Count Per Second (CPS), based on backward extrapolation of the losses, created by increasingly growing artificially imposed dead time on the data, back to zero. The method has been implemented on actual neutron noise measurements carried out in the MINERVE zero power reactor, demonstrating high accuracy (of 1–2%) in restoring the corrected count rate. - Highlights: • A new method for dead time corrections is introduced and experimentally validated. • The method does not depend on any prior calibration nor assumes any specific model. • Different dead times are imposed on the signal and the losses are extrapolated to zero. • The method is implemented and validated using neutron measurements from the MINERVE. • Result show very good correspondence to empirical results.
Correction of misclassification bias induced by the residential mobility in studies examining the link between socioeconomic environment and cancer incidence.
Bryere, Josephine; Pornet, Carole; Dejardin, Olivier; Launay, Ludivine; Guittet, Lydia; Launoy, Guy
Many international ecological studies that examine the link between social environment and cancer incidence use a deprivation index based on the subjects' address at the time of diagnosis to evaluate socioeconomic status. Thus, social past details are ignored, which leads to misclassification bias in the estimations. The objectives of this study were to include the latency delay in such estimations and to observe the effects. We adapted a previous methodology to correct estimates of the influence of socioeconomic environment on cancer incidence considering the latency delay in measuring socioeconomic status. We implemented this method using French data. We evaluated the misclassification due to social mobility with census data and corrected the relative risks. Inclusion of misclassification affected the values of relative risks, and the corrected values showed a greater departure from the value 1 than the uncorrected ones. For cancer of lung, colon-rectum, lips-mouth-pharynx, kidney and esophagus in men, the over incidence in the deprived categories was augmented by the correction. By not taking into account the latency period in measuring socioeconomic status, the burden of cancer associated with social inequality may be underestimated. Copyright © 2014 Elsevier Ltd. All rights reserved.
Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia
Worqlul, Abeyou W.; Ayana, Essayas K.; Maathuis, Ben H. P.; MacAlister, Charlotte; Philpot, William D.; Osorio Leyton, Javier M.; Steenhuis, Tammo S.
In many developing countries and remote areas of important ecosystems, good quality precipitation data are neither available nor readily accessible. Satellite observations and processing algorithms are being extensively used to produce satellite rainfall products (SREs). Nevertheless, these products are prone to systematic errors and need extensive validation before to be usable for streamflow simulations. In this study, we investigated and corrected the bias of Multi-Sensor Precipitation Estimate-Geostationary (MPEG) data. The corrected MPEG dataset was used as input to a semi-distributed hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) for simulation of discharge of the Gilgel Abay and Gumara watersheds in the Upper Blue Nile basin, Ethiopia. The result indicated that the MPEG satellite rainfall captured 81% and 78% of the gauged rainfall variability with a consistent bias of underestimating the gauged rainfall by 60%. A linear bias correction applied significantly reduced the bias while maintaining the coefficient of correlation. The simulated flow using bias corrected MPEG SRE resulted in a simulated flow comparable to the gauge rainfall for both watersheds. The study indicated the potential of MPEG SRE in water budget studies after applying a linear bias correction.
Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile
Directory of Open Access Journals (Sweden)
Emad Habib
Full Text Available Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.
Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.
Ferrazzi, Giulio; Kuklisova Murgasova, Maria; Arichi, Tomoki; Malamateniou, Christina; Fox, Matthew J; Makropoulos, Antonios; Allsop, Joanna; Rutherford, Mary; Malik, Shaihan; Aljabar, Paul; Hajnal, Joseph V
There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. Copyright © 2014 Elsevier Inc. All rights reserved.
Correction of sampling bias in a cross-sectional study of post-surgical complications.
Fluss, Ronen; Mandel, Micha; Freedman, Laurence S; Weiss, Inbal Salz; Zohar, Anat Ekka; Haklai, Ziona; Gordon, Ethel-Sherry; Simchen, Elisheva
Cross-sectional designs are often used to monitor the proportion of infections and other post-surgical complications acquired in hospitals. However, conventional methods for estimating incidence proportions when applied to cross-sectional data may provide estimators that are highly biased, as cross-sectional designs tend to include a high proportion of patients with prolonged hospitalization. One common solution is to use sampling weights in the analysis, which adjust for the sampling bias inherent in a cross-sectional design. The current paper describes in detail a method to build weights for a national survey of post-surgical complications conducted in Israel. We use the weights to estimate the probability of surgical site infections following colon resection, and validate the results of the weighted analysis by comparing them with those obtained from a parallel study with a historically prospective design. Copyright © 2012 John Wiley & Sons, Ltd.
Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model
Arumugam, S.; Libera, D.
Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.
On the photon energy moments and their 'bias' corrections in B->Xs+γ
International Nuclear Information System (INIS)
Benson, D.; Bigi, I.I.; Uraltsev, N.
Photon energy moments in B->X s +γ and the impact of experimental cuts are analyzed, including the biases exponential in the effective hardness missed in the conventional OPE. We incorporate the perturbative corrections fully implementing the Wilsonian momentum separation ab initio. This renders perturbative effects numerically suppressed while leaving heavy quark parameters and the corresponding light-cone distribution function well defined and preserving their physical properties. The moments of the distribution function are given by the heavy quark expectation values of which many have been extracted from the B->X c -bar ν decays. The quantitative estimates for the biases in the heavy quark parameters determined from the photon moments show they cannot be neglected for E cut -bar 1.85 GeV, and grow out of theory control for E cut above 2.1 GeV. Implications for the moments in the B->X c -bar ν decays at high cuts are briefly addressed
Atlas-based analysis of cardiac shape and function: correction of regional shape bias due to imaging protocol for population studies.
Medrano-Gracia, Pau; Cowan, Brett R; Bluemke, David A; Finn, J Paul; Kadish, Alan H; Lee, Daniel C; Lima, Joao A C; Suinesiaputra, Avan; Young, Alistair A
Cardiovascular imaging studies generate a wealth of data which is typically used only for individual study endpoints. By pooling data from multiple sources, quantitative comparisons can be made of regional wall motion abnormalities between different cohorts, enabling reuse of valuable data. Atlas-based analysis provides precise quantification of shape and motion differences between disease groups and normal subjects. However, subtle shape differences may arise due to differences in imaging protocol between studies. A mathematical model describing regional wall motion and shape was used to establish a coordinate system registered to the cardiac anatomy. The atlas was applied to data contributed to the Cardiac Atlas Project from two independent studies which used different imaging protocols: steady state free precession (SSFP) and gradient recalled echo (GRE) cardiovascular magnetic resonance (CMR). Shape bias due to imaging protocol was corrected using an atlas-based transformation which was generated from a set of 46 volunteers who were imaged with both protocols. Shape bias between GRE and SSFP was regionally variable, and was effectively removed using the atlas-based transformation. Global mass and volume bias was also corrected by this method. Regional shape differences between cohorts were more statistically significant after removing regional artifacts due to imaging protocol bias. Bias arising from imaging protocol can be both global and regional in nature, and is effectively corrected using an atlas-based transformation, enabling direct comparison of regional wall motion abnormalities between cohorts acquired in separate studies.
New method for eliminating the statistical bias in highly turbulent flow measurements
International Nuclear Information System (INIS)
Nakao, S.I.; Terao, Y.; Hirata, K.I.; Kitakyushu Industrial Research Institute, Fukuoka, Japan)
A simple method was developed for eliminating statistical bias which can be applied to highly turbulent flows with the sparse and nonuniform seeding conditions. Unlike the method proposed so far, a weighting function was determined based on the idea that the statistical bias could be eliminated if the asymmetric form of the probability density function of the velocity data were corrected. Moreover, the data more than three standard deviations away from the mean were discarded to remove the apparent turbulent intensity resulting from noise. The present method was applied to data obtained in the wake of a block, which provided local turbulent intensities up to about 120 percent, it was found to eliminate the statistical bias with high accuracy. 9 references
Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast
Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard
Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.
Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere
Whiteman, D. N.; Cadirola, M.; Venable, D.; Calhoun, M.; Miloshevich, L; Vermeesch, K.; Twigg, L.; Dirisu, A.; Hurst, D.; Hall, E.;
The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper
QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials.
Malyarenko, Dariya I; Wilmes, Lisa J; Arlinghaus, Lori R; Jacobs, Michael A; Huang, Wei; Helmer, Karl G; Taouli, Bachir; Yankeelov, Thomas E; Newitt, David; Chenevert, Thomas L
Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, -35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.
"The impact of uncertain threat on affective bias: Individual differences in response to ambiguity": Correction.
Reports an error in "The impact of uncertain threat on affective bias: Individual differences in response to ambiguity" by Maital Neta, Julie Cantelon, Zachary Haga, Caroline R. Mahoney, Holly A. Taylor and F. Caroline Davis ( Emotion , 2017[Dec], Vol 17[8], 1137-1143). In this article, the copyright attribution was incorrectly listed under the Creative Commons CC-BY license due to production-related error. The correct copyright should be "In the public domain." The online version of this article has been corrected. (The following abstract of the original article appeared in record 2017-40275-001.) Individuals who operate under highly stressful conditions (e.g., military personnel and first responders) are often faced with the challenge of quickly interpreting ambiguous information in uncertain and threatening environments. When faced with ambiguity, it is likely adaptive to view potentially dangerous stimuli as threatening until contextual information proves otherwise. One laboratory-based paradigm that can be used to simulate uncertain threat is known as threat of shock (TOS), in which participants are told that they might receive mild but unpredictable electric shocks while performing an unrelated task. The uncertainty associated with this potential threat induces a state of emotional arousal that is not overwhelmingly stressful, but has widespread-both adaptive and maladaptive-effects on cognitive and affective function. For example, TOS is thought to enhance aversive processing and abolish positivity bias. Importantly, in certain situations (e.g., when walking home alone at night), this anxiety can promote an adaptive state of heightened vigilance and defense mobilization. In the present study, we used TOS to examine the effects of uncertain threat on valence bias, or the tendency to interpret ambiguous social cues as positive or negative. As predicted, we found that heightened emotional arousal elicited by TOS was associated with an increased tendency to
The Threat of Common Method Variance Bias to Theory Building
Reio, Thomas G., Jr.
The need for more theory building scholarship remains one of the pressing issues in the field of HRD. Researchers can employ quantitative, qualitative, and/or mixed methods to support vital theory-building efforts, understanding however that each approach has its limitations. The purpose of this article is to explore common method variance bias as…
A new method for mapping perceptual biases across visual space.
Finlayson, Nonie J; Papageorgiou, Andriani; Schwarzkopf, D Samuel
How we perceive the environment is not stable and seamless. Recent studies found that how a person qualitatively experiences even simple visual stimuli varies dramatically across different locations in the visual field. Here we use a method we developed recently that we call multiple alternatives perceptual search (MAPS) for efficiently mapping such perceptual biases across several locations. This procedure reliably quantifies the spatial pattern of perceptual biases and also of uncertainty and choice. We show that these measurements are strongly correlated with those from traditional psychophysical methods and that exogenous attention can skew biases without affecting overall task performance. Taken together, MAPS is an efficient method to measure how an individual's perceptual experience varies across space.
Bias correction of bounded location errors in presence-only data
Hefley, Trevor J.; Brost, Brian M.; Hooten, Mevin B.
Location error occurs when the true location is different than the reported location. Because habitat characteristics at the true location may be different than those at the reported location, ignoring location error may lead to unreliable inference concerning species–habitat relationships.We explain how a transformation known in the spatial statistics literature as a change of support (COS) can be used to correct for location errors when the true locations are points with unknown coordinates contained within arbitrary shaped polygons.We illustrate the flexibility of the COS by modelling the resource selection of Whooping Cranes (Grus americana) using citizen contributed records with locations that were reported with error. We also illustrate the COS with a simulation experiment.In our analysis of Whooping Crane resource selection, we found that location error can result in up to a five-fold change in coefficient estimates. Our simulation study shows that location error can result in coefficient estimates that have the wrong sign, but a COS can efficiently correct for the bias.
Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images.
Banerjee, Abhirup; Maji, Pradipta
The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.
Bias correction and Bayesian analysis of aggregate counts in SAGE libraries
Directory of Open Access Journals (Sweden)
Briggs William M
Full Text Available Abstract Background Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.
Bias Correction of Satellite Precipitation Products (SPPs) using a User-friendly Tool: A Step in Enhancing Technical Capacity
Rushi, B. R.; Ellenburg, W. L.; Adams, E. C.; Flores, A.; Limaye, A. S.; Valdés-Pineda, R.; Roy, T.; Valdés, J. B.; Mithieu, F.; Omondi, S.
SERVIR, a joint NASA-USAID initiative, works to build capacity in Earth observation technologies in developing countries for improved environmental decision making in the arena of: weather and climate, water and disasters, food security and land use/land cover. SERVIR partners with leading regional organizations in Eastern and Southern Africa, Hindu Kush-Himalaya, Mekong region, and West Africa to achieve its objectives. SERVIR develops hydrological applications to address specific needs articulated by key stakeholders and daily rainfall estimates are a vital input for these applications. Satellite-derived rainfall is subjected to systemic biases which need to be corrected before it can be used for any hydrologic application such as real-time or seasonal forecasting. SERVIR and the SWAAT team at the University of Arizona, have co-developed an open-source and user friendly tool of rainfall bias correction approaches for SPPs. Bias correction tools were developed based on Linear Scaling and Quantile Mapping techniques. A set of SPPs, such as PERSIANN-CCS, TMPA-RT, and CMORPH, are bias corrected using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data which incorporates ground based precipitation observations. This bias correction tools also contains a component, which is included to improve monthly mean of CHIRPS using precipitation products of the Global Surface Summary of the Day (GSOD) database developed by the National Climatic Data Center (NCDC). This tool takes input from command-line which makes it user-friendly and applicable in any operating platform without prior programming skills. This presentation will focus on this bias-correction tool for SPPs, including application scenarios.
Correction of Gradient Nonlinearity Bias in Quantitative Diffusion Parameters of Renal Tissue with Intra Voxel Incoherent Motion.
Malyarenko, Dariya I; Pang, Yuxi; Senegas, Julien; Ivancevic, Marko K; Ross, Brian D; Chenevert, Thomas L
Spatially non-uniform diffusion weighting bias due to gradient nonlinearity (GNL) causes substantial errors in apparent diffusion coefficient (ADC) maps for anatomical regions imaged distant from magnet isocenter. Our previously-described approach allowed effective removal of spatial ADC bias from three orthogonal DWI measurements for mono-exponential media of arbitrary anisotropy. The present work evaluates correction feasibility and performance for quantitative diffusion parameters of the two-component IVIM model for well-perfused and nearly isotropic renal tissue. Sagittal kidney DWI scans of a volunteer were performed on a clinical 3T MRI scanner near isocenter and offset superiorly. Spatially non-uniform diffusion weighting due to GNL resulted both in shift and broadening of perfusion-suppressed ADC histograms for off-center DWI relative to unbiased measurements close to isocenter. Direction-average DW-bias correctors were computed based on the known gradient design provided by vendor. The computed bias maps were empirically confirmed by coronal DWI measurements for an isotropic gel-flood phantom. Both phantom and renal tissue ADC bias for off-center measurements was effectively removed by applying pre-computed 3D correction maps. Comparable ADC accuracy was achieved for corrections of both b -maps and DWI intensities in presence of IVIM perfusion. No significant bias impact was observed for IVIM perfusion fraction.
Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods.
Cao, Huiliang; Li, Hongsheng; Kou, Zhiwei; Shi, Yunbo; Tang, Jun; Ma, Zongmin; Shen, Chong; Liu, Jun
This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses' quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC), Quadrature Force Correction (QFC) and Coupling Stiffness Correction (CSC) methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW) value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups' output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability.
Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods
Directory of Open Access Journals (Sweden)
Huiliang Cao
Full Text Available This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses' quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC, Quadrature Force Correction (QFC and Coupling Stiffness Correction (CSC methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups' output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability.
Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods
Cao, Huiliang; Li, Hongsheng; Kou, Zhiwei; Shi, Yunbo; Tang, Jun; Ma, Zongmin; Shen, Chong; Liu, Jun
This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses' quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC), Quadrature Force Correction (QFC) and Coupling Stiffness Correction (CSC) methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW) value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups' output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability. PMID:26751455
Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval.
Raiche, Gilles; Blais, Jean-Guy
In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.
"Unreliability as a threat to understanding psychopathology: The cautionary tale of attentional bias": Correction to Rodebaugh et al. (2016).
Reports an error in "Unreliability as a threat to understanding psychopathology: The cautionary tale of attentional bias" by Thomas L. Rodebaugh, Rachel B. Scullin, Julia K. Langer, David J. Dixon, Jonathan D. Huppert, Amit Bernstein, Ariel Zvielli and Eric J. Lenze ( Journal of Abnormal Psychology , 2016[Aug], Vol 125[6], 840-851). There was an error in the Author Note concerning the support of the MacBrain Face Stimulus Set. The correct statement is provided. (The following abstract of the original article appeared in record 2016-30117-001.) The use of unreliable measures constitutes a threat to our understanding of psychopathology, because advancement of science using both behavioral and biologically oriented measures can only be certain if such measurements are reliable. Two pillars of the National Institute of Mental Health's portfolio-the Research Domain Criteria (RDoC) initiative for psychopathology and the target engagement initiative in clinical trials-cannot succeed without measures that possess the high reliability necessary for tests involving mediation and selection based on individual differences. We focus on the historical lack of reliability of attentional bias measures as an illustration of how reliability can pose a threat to our understanding. Our own data replicate previous findings of poor reliability for traditionally used scores, which suggests a serious problem with the ability to test theories regarding attentional bias. This lack of reliability may also suggest problems with the assumption (in both theory and the formula for the scores) that attentional bias is consistent and stable across time. In contrast, measures accounting for attention as a dynamic process in time show good reliability in our data. The field is sorely in need of research reporting findings and reliability for attentional bias scores using multiple methods, including those focusing on dynamic processes over time. We urge researchers to test and report reliability of
Statistical methods for accurately determining criticality code bias
International Nuclear Information System (INIS)
Trumble, E.F.; Kimball, K.D.
A system of statistically treating validation calculations for the purpose of determining computer code bias is provided in this paper. The following statistical treatments are described: weighted regression analysis, lower tolerance limit, lower tolerance band, and lower confidence band. These methods meet the criticality code validation requirements of ANS 8.1. 8 refs., 5 figs., 4 tabs
Reproducibility and day time bias correction of optoelectronic leg volumetry: a prospective cohort study.
Engelberger, Rolf P; Blazek, Claudia; Amsler, Felix; Keo, Hong H; Baumann, Frédéric; Blättler, Werner; Baumgartner, Iris; Willenberg, Torsten
Leg edema is a common manifestation of various underlying pathologies. Reliable measurement tools are required to quantify edema and monitor therapeutic interventions. Aim of the present work was to investigate the reproducibility of optoelectronic leg volumetry over 3 weeks' time period and to eliminate daytime related within-individual variability. Optoelectronic leg volumetry was performed in 63 hairdressers (mean age 45 ± 16 years, 85.7% female) in standing position twice within a minute for each leg and repeated after 3 weeks. Both lower leg (legBD) and whole limb (limbBF) volumetry were analysed. Reproducibility was expressed as analytical and within-individual coefficients of variance (CVA, CVW), and as intra-class correlation coefficients (ICC). A total of 492 leg volume measurements were analysed. Both legBD and limbBF volumetry were highly reproducible with CVA of 0.5% and 0.7%, respectively. Within-individual reproducibility of legBD and limbBF volumetry over a three weeks' period was high (CVW 1.3% for both; ICC 0.99 for both). At both visits, the second measurement revealed a significantly higher volume compared to the first measurement with a mean increase of 7.3 ml ± 14.1 (0.33% ± 0.58%) for legBD and 30.1 ml ± 48.5 ml (0.52% ± 0.79%) for limbBF volume. A significant linear correlation between absolute and relative leg volume differences and the difference of exact day time of measurement between the two study visits was found (P correction formula permitted further improvement of CVW. Leg volume changes can be reliably assessed by optoelectronic leg volumetry at a single time point and over a 3 weeks' time period. However, volumetry results are biased by orthostatic and daytime-related volume changes. The bias for day-time related volume changes can be minimized by a time-correction formula.
CEO emotional bias and investment decision, Bayesian network method
Directory of Open Access Journals (Sweden)
Jarboui Anis
Full Text Available This research examines the determinants of firms' investment introducing a behavioral perspective that has received little attention in corporate finance literature. The following central hypothesis emerges from a set of recently developed theories: Investment decisions are influenced not only by their fundamentals but also depend on some other factors. One factor is the biasness of any CEO to their investment, biasness depends on the cognition and emotions, because some leaders use them as heuristic for the investment decision instead of fundamentals. This paper shows how CEO emotional bias (optimism, loss aversion and overconfidence affects the investment decisions. The proposed model of this paper uses Bayesian Network Method to examine this relationship. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 100 Tunisian executives. Our results have revealed that the behavioral analysis of investment decision implies leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its investment choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.
Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity
Blakenship, Clay; Zavodsky, Bradley; Blackwell, William
The Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. Forecasts are against ERA reanalyses.
Productivity changes in OECD healthcare systems: bias-corrected Malmquist productivity approach.
Kim, Younhee; Oh, Dong-Hyun; Kang, Minah
This study evaluates productivity changes in the healthcare systems of 30 Organization for Economic Co-operation and Development (OECD) countries over the 2002-2012 periods. The bootstrapped Malmquist approach is used to estimate bias-corrected indices of healthcare performance in productivity, efficiency and technology by modifying the original distance functions. Two inputs (health expenditure and school life expectancy) and two outputs (life expectancy at birth and infant mortality rate) are used to calculate productivity growth. There are no perceptible trends in productivity changes over the 2002-2012 periods, but positive productivity improvement has been noticed for most OECD countries. The result also informs considerable variations in annual productivity scores across the countries. Average annual productivity growth is evenly yielded by efficiency and technical changes, but both changes run somewhat differently across the years. The results of this study assert that policy reforms in OECD countries have improved productivity growth in healthcare systems over the past decade. Countries that lag behind in productivity growth should benchmark peer countries' practices to increase performance by prioritizing an achievable trajectory based on socioeconomic conditions. For example, relatively inefficient countries in this study indicate higher income inequality, corresponding to inequality and health outcomes studies. Although income inequality and globalization are not direct measures to estimate healthcare productivity in this study, these issues could be latent factors to explain cross-country healthcare productivity for future research. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Estimation and correction of surface wind-stress bias in the Tropical Pacific with the Ensemble Kalman Filter
Leeuwenburgh, O.
The assimilation of high-quality in situ data into ocean models is known to lead to imbalanced analyses and spurious circulations when the model dynamics or the forcing contain systematic errors. Use of a bias estimation and correction scheme has been shown to significantly improve the balance of
Study of the orbital correction method
International Nuclear Information System (INIS)
Meserve, R.A.
Two approximations of interest in atomic, molecular, and solid state physics are explored. First, a procedure for calculating an approximate Green's function for use in perturbation theory is derived. In lowest order it is shown to be equivalent to treating the contribution of the bound states of the unperturbed Hamiltonian exactly and representing the continuum contribution by plane waves orthogonalized to the bound states (OPW's). If the OPW approximation were inadequate, the procedure allows for systematic improvement of the approximation. For comparison purposes an exact but more limited procedure for performing second-order perturbation theory, one that involves solving an inhomogeneous differential equation, is also derived. Second, the Kohn-Sham many-electron formalism is discussed and formulae are derived and discussed for implementing perturbation theory within the formalism so as to find corrections to the total energy of a system through second order in the perturbation. Both approximations were used in the calculation of the polarizability of helium, neon, and argon. The calculation included direct and exchange effects by the Kohn-Sham method and full self-consistency was demanded. The results using the differential equation method yielded excellent agreement with the coupled Hartree-Fock results of others and with experiment. Moreover, the OPW approximation yielded satisfactory comparison with the results of calculation by the exact differential equation method. Finally, both approximations were used in the calculation of properties of hydrogen fluoride and methane. The appendix formulates a procedure using group theory and the internal coordinates of a molecular system to simplify the calculation of vibrational frequencies
Gain compensation technique by bias correction in arrays of Silicon Photomultipliers using fully differential fast shaper
Energy Technology Data Exchange (ETDEWEB)
Baszczyk, M., E-mail: email@example.com [AGH University of Science and Technology, Department of Electronics, Krakow (Poland); Dorosz, P.; Glab, S.; Kucewicz, W. [AGH University of Science and Technology, Department of Electronics, Krakow (Poland); Mik, L. [AGH University of Science and Technology, Department of Electronics, Krakow (Poland); State Higher Vocational School, Tarnow (Poland); Sapor, M. [AGH University of Science and Technology, Department of Electronics, Krakow (Poland)
Proposed algorithm compensates the gain by changing the bias voltage of Silicon Photomultipliers (SiPM). The signal from SiPM is amplified in fully differential preamplifier then is formed in time by the fully differential fast shaper. The compensation method was tested with four channels common cathode multi-pixel photon counter from Hamamatsu. The measurement system requires only one high voltage power supply. The polarization voltage is adjusted individually in each channel indirectly by tuning the output common mode voltage (VOCM) of fully differential amplifier. The changes of VOCM affect the input voltage through the feedback network. Actual gain of the SiPM is calculated by measuring the mean amplitude of the signal resulting from detection of single photoelectron. The VOCM is adjusted by DAC so as to reach the desired value of gain by each channel individually. The advantage of the algorithm is the possibility to set the bias of each SiPM in the array independently so they all could operate in very similar conditions (have similar gain and dark count rate). The algorithm can compensate the variations of gain of SiPM by using thermally generated pulses. There is no need to use additional current to voltage conversion which could introduce extra noises.
Gain compensation technique by bias correction in arrays of Silicon Photomultipliers using fully differential fast shaper
Baszczyk, M.; Dorosz, P.; Glab, S.; Kucewicz, W.; Mik, L.; Sapor, M.
Proposed algorithm compensates the gain by changing the bias voltage of Silicon Photomultipliers (SiPM). The signal from SiPM is amplified in fully differential preamplifier then is formed in time by the fully differential fast shaper. The compensation method was tested with four channels common cathode multi-pixel photon counter from Hamamatsu. The measurement system requires only one high voltage power supply. The polarization voltage is adjusted individually in each channel indirectly by tuning the output common mode voltage (VOCM) of fully differential amplifier. The changes of VOCM affect the input voltage through the feedback network. Actual gain of the SiPM is calculated by measuring the mean amplitude of the signal resulting from detection of single photoelectron. The VOCM is adjusted by DAC so as to reach the desired value of gain by each channel individually. The advantage of the algorithm is the possibility to set the bias of each SiPM in the array independently so they all could operate in very similar conditions (have similar gain and dark count rate). The algorithm can compensate the variations of gain of SiPM by using thermally generated pulses. There is no need to use additional current to voltage conversion which could introduce extra noises.
Gain compensation technique by bias correction in arrays of Silicon Photomultipliers using fully differential fast shaper
International Nuclear Information System (INIS)
Baszczyk, M.; Dorosz, P.; Glab, S.; Kucewicz, W.; Mik, L.; Sapor, M.
Proposed algorithm compensates the gain by changing the bias voltage of Silicon Photomultipliers (SiPM). The signal from SiPM is amplified in fully differential preamplifier then is formed in time by the fully differential fast shaper. The compensation method was tested with four channels common cathode multi-pixel photon counter from Hamamatsu. The measurement system requires only one high voltage power supply. The polarization voltage is adjusted individually in each channel indirectly by tuning the output common mode voltage (VOCM) of fully differential amplifier. The changes of VOCM affect the input voltage through the feedback network. Actual gain of the SiPM is calculated by measuring the mean amplitude of the signal resulting from detection of single photoelectron. The VOCM is adjusted by DAC so as to reach the desired value of gain by each channel individually. The advantage of the algorithm is the possibility to set the bias of each SiPM in the array independently so they all could operate in very similar conditions (have similar gain and dark count rate). The algorithm can compensate the variations of gain of SiPM by using thermally generated pulses. There is no need to use additional current to voltage conversion which could introduce extra noises.
A New Dyslexia Reading Method and Visual Correction Position Method.
Manilla, George T; de Braga, Joe
Pediatricians and educators may interact daily with several dyslexic patients or students. One dyslexic author accidently developed a personal, effective, corrective reading method. Its effectiveness was evaluated in 3 schools. One school utilized 8 demonstration special education students. Over 3 months, one student grew one third year, 3 grew 1 year, and 4 grew 2 years. In another school, 6 sixth-, seventh-, and eighth-grade classroom teachers followed 45 treated dyslexic students. They all excelled and progressed beyond their classroom peers in 4 months. Using cyclovergence upper gaze, dyslexic reading problems disappeared at one of the Positional Reading Arc positions of 30°, 60°, 90°, 120°, or 150° for 10 dyslexics. Positional Reading Arc on 112 students of the second through eighth grades showed words read per minute, reading errors, and comprehension improved. Dyslexia was visually corrected by use of a new reading method and Positional Reading Arc positions.
A two-phase sampling survey for nonresponse and its paradata to correct nonresponse bias in a health surveillance survey.
Santin, G; Bénézet, L; Geoffroy-Perez, B; Bouyer, J; Guéguen, A
The decline in participation rates in surveys, including epidemiological surveillance surveys, has become a real concern since it may increase nonresponse bias. The aim of this study is to estimate the contribution of a complementary survey among a subsample of nonrespondents, and the additional contribution of paradata in correcting for nonresponse bias in an occupational health surveillance survey. In 2010, 10,000 workers were randomly selected and sent a postal questionnaire. Sociodemographic data were available for the whole sample. After data collection of the questionnaires, a complementary survey among a random subsample of 500 nonrespondents was performed using a questionnaire administered by an interviewer. Paradata were collected for the complete subsample of the complementary survey. Nonresponse bias in the initial sample and in the combined samples were assessed using variables from administrative databases available for the whole sample, not subject to differential measurement errors. Corrected prevalences by reweighting technique were estimated by first using the initial survey alone and then the initial and complementary surveys combined, under several assumptions regarding the missing data process. Results were compared by computing relative errors. The response rates of the initial and complementary surveys were 23.6% and 62.6%, respectively. For the initial and the combined surveys, the relative errors decreased after correction for nonresponse on sociodemographic variables. For the combined surveys without paradata, relative errors decreased compared with the initial survey. The contribution of the paradata was weak. When a complex descriptive survey has a low response rate, a short complementary survey among nonrespondents with a protocol which aims to maximize the response rates, is useful. The contribution of sociodemographic variables in correcting for nonresponse bias is important whereas the additional contribution of paradata in
Nowcasting Surface Meteorological Parameters Using Successive Correction Method
National Research Council Canada - National Science Library
Henmi, Teizi
The successive correction method was examined and evaluated statistically as a nowcasting method for surface meteorological parameters including temperature, dew point temperature, and horizontal wind vector components...
A method to correct coordinate distortion in EBSD maps
International Nuclear Information System (INIS)
Zhang, Y.B.; Elbrønd, A.; Lin, F.X.
Drift during electron backscatter diffraction mapping leads to coordinate distortions in resulting orientation maps, which affects, in some cases significantly, the accuracy of analysis. A method, thin plate spline, is introduced and tested to correct such coordinate distortions in the maps after the electron backscatter diffraction measurements. The accuracy of the correction as well as theoretical and practical aspects of using the thin plate spline method is discussed in detail. By comparing with other correction methods, it is shown that the thin plate spline method is most efficient to correct different local distortions in the electron backscatter diffraction maps. - Highlights: • A new method is suggested to correct nonlinear spatial distortion in EBSD maps. • The method corrects EBSD maps more precisely than presently available methods. • Errors less than 1–2 pixels are typically obtained. • Direct quantitative analysis of dynamic data are available after this correction
CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.
Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M
Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. firstname.lastname@example.com. Supplementary data are available at Bioinformatics online.
Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix
Bun, M.J.G.
Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J.
New decoding methods of interleaved burst error-correcting codes
Nakano, Y.; Kasahara, M.; Namekawa, T.
A probabilistic method of single burst error correction, using the syndrome correlation of subcodes which constitute the interleaved code, is presented. This method makes it possible to realize a high capability of burst error correction with less decoding delay. By generalizing this method it is possible to obtain probabilistic method of multiple (m-fold) burst error correction. After estimating the burst error positions using syndrome correlation of subcodes which are interleaved m-fold burst error detecting codes, this second method corrects erasure errors in each subcode and m-fold burst errors. The performance of these two methods is analyzed via computer simulation, and their effectiveness is demonstrated.
SU-F-I-80: Correction for Bias in a Channelized Hotelling Model Observer Caused by Temporally Variable Non-Stationary Noise
International Nuclear Information System (INIS)
Favazza, C; Fetterly, K
Purpose: Application of a channelized Hotelling model observer (CHO) over a wide range of x-ray angiography detector target dose (DTD) levels demonstrated substantial bias for conditions yielding low detectability indices (d'), including low DTD and small test objects. The purpose of this work was to develop theory and methods to correct this bias. Methods: A hypothesis was developed wherein the measured detectability index (d'b) for a known test object is positively biased by temporally variable non-stationary noise in the images. Hotelling's T2 test statistic provided the foundation for a mathematical theory which accounts for independent contributions to the measured d'b value from both the test object (d'o) and non-stationary noise (d'ns). Experimental methods were developed to directly estimate d'o by determining d'ns and subtracting it from d'b, in accordance with the theory. Specifically, d'ns was determined from two sets of images from which the traditional test object was withheld. This method was applied to angiography images with DTD levels in the range 0 to 240 nGy and for disk-shaped iodine-based contrast targets with diameters 0.5 to 4.0 mm. Results: Bias in d' was evidenced by d'b values which exceeded values expected from a quantum limited imaging system and decreasing object size and DTD. d'ns increased with decreasing DTD, reaching a maximum of 2.6 for DTD = 0. Bias-corrected d'o estimates demonstrated sub-quantum limited performance of the x-ray angiography for low DTD. Findings demonstrated that the source of non-stationary noise was detector electronic readout noise. Conclusion: Theory and methods to estimate and correct bias in CHO measurements from temporally variable non-stationary noise were presented. The temporal non-stationary noise was shown to be due to electronic readout noise. This method facilitates accurate estimates of d' values over a large range of object size and detector target dose.
SU-F-I-80: Correction for Bias in a Channelized Hotelling Model Observer Caused by Temporally Variable Non-Stationary Noise
Energy Technology Data Exchange (ETDEWEB)
Favazza, C; Fetterly, K [Mayo Clinic, Rochester, MN (United States)
Purpose: Application of a channelized Hotelling model observer (CHO) over a wide range of x-ray angiography detector target dose (DTD) levels demonstrated substantial bias for conditions yielding low detectability indices (d'), including low DTD and small test objects. The purpose of this work was to develop theory and methods to correct this bias. Methods: A hypothesis was developed wherein the measured detectability index (d'b) for a known test object is positively biased by temporally variable non-stationary noise in the images. Hotelling's T2 test statistic provided the foundation for a mathematical theory which accounts for independent contributions to the measured d'b value from both the test object (d'o) and non-stationary noise (d'ns). Experimental methods were developed to directly estimate d'o by determining d'ns and subtracting it from d'b, in accordance with the theory. Specifically, d'ns was determined from two sets of images from which the traditional test object was withheld. This method was applied to angiography images with DTD levels in the range 0 to 240 nGy and for disk-shaped iodine-based contrast targets with diameters 0.5 to 4.0 mm. Results: Bias in d' was evidenced by d'b values which exceeded values expected from a quantum limited imaging system and decreasing object size and DTD. d'ns increased with decreasing DTD, reaching a maximum of 2.6 for DTD = 0. Bias-corrected d'o estimates demonstrated sub-quantum limited performance of the x-ray angiography for low DTD. Findings demonstrated that the source of non-stationary noise was detector electronic readout noise. Conclusion: Theory and methods to estimate and correct bias in CHO measurements from temporally variable non-stationary noise were presented. The temporal non-stationary noise was shown to be due to electronic readout noise. This method facilitates accurate estimates of d' values over a large range of object size and detector target dose.
Methods of orbit correction system optimization
International Nuclear Information System (INIS)
Chao, Yu-Chiu.
Extracting optimal performance out of an orbit correction system is an important component of accelerator design and evaluation. The question of effectiveness vs. economy, however, is not always easily tractable. This is especially true in cases where betatron function magnitude and phase advance do not have smooth or periodic dependencies on the physical distance. In this report a program is presented using linear algebraic techniques to address this problem. A systematic recipe is given, supported with quantitative criteria, for arriving at an orbit correction system design with the optimal balance between performance and economy. The orbit referred to in this context can be generalized to include angle, path length, orbit effects on the optical transfer matrix, and simultaneous effects on multiple pass orbits
Validation of the AMSU-B Bias Corrections Based on Satellite Measurements from SSM/T-2
Kolodner, Marc A.
The NOAA-15 Advanced Microwave Sounding Unit-B (AMSU-B) was designed in the same spirit as the Special Sensor Microwave Water Vapor Profiler (SSM/T-2) on board the DMSP F11-14 satellites, to perform remote sensing of spatial and temporal variations in mid and upper troposphere humidity. While the SSM/T-2 instruments have a 48 km spatial resolution at nadir and 28 beam positions per scan, AMSU-B provides an improvement with a 16 km spatial resolution at nadir and 90 beam positions per scan. The AMSU-B instrument, though, has been experiencing radio frequency interference (RFI) contamination from the NOAA-15 transmitters whose effect is dependent upon channel, geographic location, and current spacecraft antenna configuration. This has lead to large cross-track biases reaching as high as 100 Kelvin for channel 17 (150 GHz) and 50 Kelvin for channel 19 (183 +/-3 GHz). NOAA-NESDIS has recently provided a series of bias corrections for AMSU-B data starting from March, 1999. These corrections are available for each of the five channels, for every third field of view, and for three cycles within an eight second period. There is also a quality indicator in each data record to indicate whether or not the bias corrections should be applied. As a precursor to performing retrievals of mid and upper troposphere humidity, a validation study is performed by statistically analyzing the differences between the F14 SSM/T-2 and the bias corrected AMSU-B brightness temperatures for three months in the spring of 1999.
Common method biases in behavioral research: a critical review of the literature and recommended remedies.
Podsakoff, Philip M; MacKenzie, Scott B; Lee, Jeong-Yeon; Podsakoff, Nathan P
Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
A method to correct coordinate distortion in EBSD maps
DEFF Research Database (Denmark)
Zhang, Yubin; Elbrønd, Andreas Benjamin; Lin, Fengxiang
Drift during electron backscatter diffraction mapping leads to coordinate distortions in resulting orientation maps, which affects, in some cases significantly, the accuracy of analysis. A method, thin plate spline, is introduced and tested to correct such coordinate distortions in the maps after...... the electron backscatter diffraction measurements. The accuracy of the correction as well as theoretical and practical aspects of using the thin plate spline method is discussed in detail. By comparing with other correction methods, it is shown that the thin plate spline method is most efficient to correct...
Local defect correction for boundary integral equation methods
Kakuba, G.; Anthonissen, M.J.H.
The aim in this paper is to develop a new local defect correction approach to gridding for problems with localised regions of high activity in the boundary element method. The technique of local defect correction has been studied for other methods as finite difference methods and finite volume
Attenuation correction method for single photon emission CT
Energy Technology Data Exchange (ETDEWEB)
Morozumi, Tatsuru; Nakajima, Masato [Keio Univ., Yokohama (Japan). Faculty of Science and Technology; Ogawa, Koichi; Yuta, Shinichi
A correction method (Modified Correction Matrix method) is proposed to implement iterative correction by exactly measuring attenuation constant distribution in a test body, calculating a correction factor for every picture element, then multiply the image by these factors. Computer simulation for the comparison of the results showed that the proposed method was specifically more effective to an application to the test body, in which the rate of attenuation constant change is large, than the conventional correction matrix method. Since the actual measurement data always contain quantum noise, the noise was taken into account in the simulation. However, the correction effect was large even under the noise. For verifying its clinical effectiveness, the experiment using an acrylic phantom was also carried out. As the result, the recovery of image quality in the parts with small attenuation constant was remarkable as compared with the conventional method.
Correction of confounding bias in non-randomized studies by appropriate weighting.
Schmoor, Claudia; Gall, Christine; Stampf, Susanne; Graf, Erika
In non-randomized studies, the assessment of a causal effect of treatment or exposure on outcome is hampered by possible confounding. Applying multiple regression models including the effects of treatment and covariates on outcome is the well-known classical approach to adjust for confounding. In recent years other approaches have been promoted. One of them is based on the propensity score and considers the effect of possible confounders on treatment as a relevant criterion for adjustment. Another proposal is based on using an instrumental variable. Here inference relies on a factor, the instrument, which affects treatment but is thought to be otherwise unrelated to outcome, so that it mimics randomization. Each of these approaches can basically be interpreted as a simple reweighting scheme, designed to address confounding. The procedures will be compared with respect to their fundamental properties, namely, which bias they aim to eliminate, which effect they aim to estimate, and which parameter is modelled. We will expand our overview of methods for analysis of non-randomized studies to methods for analysis of randomized controlled trials and show that analyses of both study types may target different effects and different parameters. The considerations will be illustrated using a breast cancer study with a so-called Comprehensive Cohort Study design, including a randomized controlled trial and a non-randomized study in the same patient population as sub-cohorts. This design offers ideal opportunities to discuss and illustrate the properties of the different approaches. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Evaluation of a scattering correction method for high energy tomography
Tisseur, David; Bhatia, Navnina; Estre, Nicolas; Berge, Léonie; Eck, Daniel; Payan, Emmanuel
One of the main drawbacks of Cone Beam Computed Tomography (CBCT) is the contribution of the scattered photons due to the object and the detector. Scattered photons are deflected from their original path after their interaction with the object. This additional contribution of the scattered photons results in increased measured intensities, since the scattered intensity simply adds to the transmitted intensity. This effect is seen as an overestimation in the measured intensity thus corresponding to an underestimation of absorption. This results in artifacts like cupping, shading, streaks etc. on the reconstructed images. Moreover, the scattered radiation provides a bias for the quantitative tomography reconstruction (for example atomic number and volumic mass measurement with dual-energy technique). The effect can be significant and difficult in the range of MeV energy using large objects due to higher Scatter to Primary Ratio (SPR). Additionally, the incident high energy photons which are scattered by the Compton effect are more forward directed and hence more likely to reach the detector. Moreover, for MeV energy range, the contribution of the photons produced by pair production and Bremsstrahlung process also becomes important. We propose an evaluation of a scattering correction technique based on the method named Scatter Kernel Superposition (SKS). The algorithm uses a continuously thickness-adapted kernels method. The analytical parameterizations of the scatter kernels are derived in terms of material thickness, to form continuously thickness-adapted kernel maps in order to correct the projections. This approach has proved to be efficient in producing better sampling of the kernels with respect to the object thickness. This technique offers applicability over a wide range of imaging conditions and gives users an additional advantage. Moreover, since no extra hardware is required by this approach, it forms a major advantage especially in those cases where
Non-linear corrections to the cosmological matter power spectrum and scale-dependent galaxy bias: implications for parameter estimation
International Nuclear Information System (INIS)
Hamann, Jan; Hannestad, Steen; Melchiorri, Alessandro; Wong, Yvonne Y Y
We explore and compare the performances of two non-linear correction and scale-dependent biasing models for the extraction of cosmological information from galaxy power spectrum data, especially in the context of beyond-ΛCDM (CDM: cold dark matter) cosmologies. The first model is the well known Q model, first applied in the analysis of Two-degree Field Galaxy Redshift Survey data. The second, the P model, is inspired by the halo model, in which non-linear evolution and scale-dependent biasing are encapsulated in a single non-Poisson shot noise term. We find that while the two models perform equally well in providing adequate correction for a range of galaxy clustering data in standard ΛCDM cosmology and in extensions with massive neutrinos, the Q model can give unphysical results in cosmologies containing a subdominant free-streaming dark matter whose temperature depends on the particle mass, e.g., relic thermal axions, unless a suitable prior is imposed on the correction parameter. This last case also exposes the danger of analytic marginalization, a technique sometimes used in the marginalization of nuisance parameters. In contrast, the P model suffers no undesirable effects, and is the recommended non-linear correction model also because of its physical transparency
Non-linear corrections to the cosmological matter power spectrum and scale-dependent galaxy bias: implications for parameter estimation
Hamann, Jan; Hannestad, Steen; Melchiorri, Alessandro; Wong, Yvonne Y. Y.
We explore and compare the performances of two non-linear correction and scale-dependent biasing models for the extraction of cosmological information from galaxy power spectrum data, especially in the context of beyond-ΛCDM (CDM: cold dark matter) cosmologies. The first model is the well known Q model, first applied in the analysis of Two-degree Field Galaxy Redshift Survey data. The second, the P model, is inspired by the halo model, in which non-linear evolution and scale-dependent biasing are encapsulated in a single non-Poisson shot noise term. We find that while the two models perform equally well in providing adequate correction for a range of galaxy clustering data in standard ΛCDM cosmology and in extensions with massive neutrinos, the Q model can give unphysical results in cosmologies containing a subdominant free-streaming dark matter whose temperature depends on the particle mass, e.g., relic thermal axions, unless a suitable prior is imposed on the correction parameter. This last case also exposes the danger of analytic marginalization, a technique sometimes used in the marginalization of nuisance parameters. In contrast, the P model suffers no undesirable effects, and is the recommended non-linear correction model also because of its physical transparency.
A Correction Method for UAV Helicopter Airborne Temperature and Humidity Sensor
Directory of Open Access Journals (Sweden)
Longqing Fan
Full Text Available This paper presents a correction method for UAV helicopter airborne temperature and humidity including an error correction scheme and a bias-calibration scheme. As rotor downwash flow brings measurement error on helicopter airborne sensors inevitably, the error correction scheme constructs a model between the rotor induced velocity and temperature and humidity by building the heat balance equation for platinum resistor temperature sensor and the pressure correction term for humidity sensor. The induced velocity of a spatial point below the rotor disc plane can be calculated by the sum of the induced velocities excited by center line vortex, rotor disk vortex, and skew cylinder vortex based on the generalized vortex theory. In order to minimize the systematic biases, the bias-calibration scheme adopts a multiple linear regression to achieve a systematically consistent result with the tethered balloon profiles. Two temperature and humidity sensors were mounted on "Z-5" UAV helicopter in the field experiment. Overall, the result of applying the calibration method shows that the temperature and relative humidity obtained by UAV helicopter closely align with tethered balloon profiles in providing measurements of the temperature profiles and humidity profiles within marine atmospheric boundary layers.
Addressing small sample size bias in multiple-biomarker trials: Inclusion of biomarker-negative patients and Firth correction.
Habermehl, Christina; Benner, Axel; Kopp-Schneider, Annette
In recent years, numerous approaches for biomarker-based clinical trials have been developed. One of these developments are multiple-biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low-prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker-negative patients at the screening stage. The small sample sizes may make it unfeasible to analyze the subtrials individually. This imposes the need to develop new approaches for the analysis of such trials. With an expected large group of biomarker-negative patients, it seems reasonable to explore options to benefit from including them in such trials. We consider advantages and disadvantages of the inclusion of biomarker-negative patients in a multiple-biomarker trial with a survival endpoint. We discuss design options that include biomarker-negative patients in the study and address the issue of small sample size bias in such trials. We carry out a simulation study for a design where biomarker-negative patients are kept in the study and are treated with standard of care. We compare three different analysis approaches based on the Cox model to examine if the inclusion of biomarker-negative patients can provide a benefit with respect to bias and variance of the treatment effect estimates. We apply the Firth correction to reduce the small sample size bias. The results of the simulation study suggest that for small sample situations, the Firth correction should be applied to adjust for the small sample size bias. Additional to the Firth penalty, the inclusion of biomarker-negative patients in the analysis can lead to further but small improvements in bias and standard deviation of the estimates. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Potassium-based algorithm allows correction for the hematocrit bias in quantitative analysis of caffeine and its major metabolite in dried blood spots.
De Kesel, Pieter M M; Capiau, Sara; Stove, Veronique V; Lambert, Willy E; Stove, Christophe P
Although dried blood spot (DBS) sampling is increasingly receiving interest as a potential alternative to traditional blood sampling, the impact of hematocrit (Hct) on DBS results is limiting its final breakthrough in routine bioanalysis. To predict the Hct of a given DBS, potassium (K(+)) proved to be a reliable marker. The aim of this study was to evaluate whether application of an algorithm, based upon predicted Hct or K(+) concentrations as such, allowed correction for the Hct bias. Using validated LC-MS/MS methods, caffeine, chosen as a model compound, was determined in whole blood and corresponding DBS samples with a broad Hct range (0.18-0.47). A reference subset (n = 50) was used to generate an algorithm based on K(+) concentrations in DBS. Application of the developed algorithm on an independent test set (n = 50) alleviated the assay bias, especially at lower Hct values. Before correction, differences between DBS and whole blood concentrations ranged from -29.1 to 21.1%. The mean difference, as obtained by Bland-Altman comparison, was -6.6% (95% confidence interval (CI), -9.7 to -3.4%). After application of the algorithm, differences between corrected and whole blood concentrations lay between -19.9 and 13.9% with a mean difference of -2.1% (95% CI, -4.5 to 0.3%). The same algorithm was applied to a separate compound, paraxanthine, which was determined in 103 samples (Hct range, 0.17-0.47), yielding similar results. In conclusion, a K(+)-based algorithm allows correction for the Hct bias in the quantitative analysis of caffeine and its metabolite paraxanthine.
Comparison of bias-corrected covariance estimators for MMRM analysis in longitudinal data with dropouts.
Gosho, Masahiko; Hirakawa, Akihiro; Noma, Hisashi; Maruo, Kazushi; Sato, Yasunori
In longitudinal clinical trials, some subjects will drop out before completing the trial, so their measurements towards the end of the trial are not obtained. Mixed-effects models for repeated measures (MMRM) analysis with "unstructured" (UN) covariance structure are increasingly common as a primary analysis for group comparisons in these trials. Furthermore, model-based covariance estimators have been routinely used for testing the group difference and estimating confidence intervals of the difference in the MMRM analysis using the UN covariance. However, using the MMRM analysis with the UN covariance could lead to convergence problems for numerical optimization, especially in trials with a small-sample size. Although the so-called sandwich covariance estimator is robust to misspecification of the covariance structure, its performance deteriorates in settings with small-sample size. We investigated the performance of the sandwich covariance estimator and covariance estimators adjusted for small-sample bias proposed by Kauermann and Carroll ( J Am Stat Assoc 2001; 96: 1387-1396) and Mancl and DeRouen ( Biometrics 2001; 57: 126-134) fitting simpler covariance structures through a simulation study. In terms of the type 1 error rate and coverage probability of confidence intervals, Mancl and DeRouen's covariance estimator with compound symmetry, first-order autoregressive (AR(1)), heterogeneous AR(1), and antedependence structures performed better than the original sandwich estimator and Kauermann and Carroll's estimator with these structures in the scenarios where the variance increased across visits. The performance based on Mancl and DeRouen's estimator with these structures was nearly equivalent to that based on the Kenward-Roger method for adjusting the standard errors and degrees of freedom with the UN structure. The model-based covariance estimator with the UN structure under unadjustment of the degrees of freedom, which is frequently used in applications
A practical procedure to improve the accuracy of radiochromic film dosimetry. A integration with a correction method of uniformity correction and a red/blue correction method
International Nuclear Information System (INIS)
Uehara, Ryuzo; Tachibana, Hidenobu; Ito, Yasushi; Yoshino, Shinichi; Matsubayashi, Fumiyasu; Sato, Tomoharu
It has been reported that the light scattering could worsen the accuracy of dose distribution measurement using a radiochromic film. The purpose of this study was to investigate the accuracy of two different films, EDR2 and EBT2, as film dosimetry tools. The effectiveness of a correction method for the non-uniformity caused from EBT2 film and the light scattering was also evaluated. In addition the efficacy of this correction method integrated with the red/blue correction method was assessed. EDR2 and EBT2 films were read using a flatbed charge-coupled device scanner (EPSON 10000 G). Dose differences on the axis perpendicular to the scanner lamp movement axis were within 1% with EDR2, but exceeded 3% (Maximum: +8%) with EBT2. The non-uniformity correction method, after a single film exposure, was applied to the readout of the films. A corrected dose distribution data was subsequently created. The correction method showed more than 10%-better pass ratios in dose difference evaluation than when the correction method was not applied. The red/blue correction method resulted in 5%-improvement compared with the standard procedure that employed red color only. The correction method with EBT2 proved to be able to rapidly correct non-uniformity, and has potential for routine clinical intensity modulated radiation therapy (IMRT) dose verification if the accuracy of EBT2 is required to be similar to that of EDR2. The use of red/blue correction method may improve the accuracy, but we recommend we should use the red/blue correction method carefully and understand the characteristics of EBT2 for red color only and the red/blue correction method. (author)
[A practical procedure to improve the accuracy of radiochromic film dosimetry: a integration with a correction method of uniformity correction and a red/blue correction method].
Uehara, Ryuzo; Tachibana, Hidenobu; Ito, Yasushi; Yoshino, Shinichi; Matsubayashi, Fumiyasu; Sato, Tomoharu
It has been reported that the light scattering could worsen the accuracy of dose distribution measurement using a radiochromic film. The purpose of this study was to investigate the accuracy of two different films, EDR2 and EBT2, as film dosimetry tools. The effectiveness of a correction method for the non-uniformity caused from EBT2 film and the light scattering was also evaluated. In addition the efficacy of this correction method integrated with the red/blue correction method was assessed. EDR2 and EBT2 films were read using a flatbed charge-coupled device scanner (EPSON 10000G). Dose differences on the axis perpendicular to the scanner lamp movement axis were within 1% with EDR2, but exceeded 3% (Maximum: +8%) with EBT2. The non-uniformity correction method, after a single film exposure, was applied to the readout of the films. A corrected dose distribution data was subsequently created. The correction method showed more than 10%-better pass ratios in dose difference evaluation than when the correction method was not applied. The red/blue correction method resulted in 5%-improvement compared with the standard procedure that employed red color only. The correction method with EBT2 proved to be able to rapidly correct non-uniformity, and has potential for routine clinical IMRT dose verification if the accuracy of EBT2 is required to be similar to that of EDR2. The use of red/blue correction method may improve the accuracy, but we recommend we should use the red/blue correction method carefully and understand the characteristics of EBT2 for red color only and the red/blue correction method.
An empirical study on memory bias situations and correction strategies in ERP effort estimation
Erasmus, I.P.; Daneva, Maia; Amrahamsson, Pekka; Corral, Luis; Olivo, Markku; Russo, Barbara
An Enterprise Resource Planning (ERP) project estimation process often relies on experts of various backgrounds to contribute judgments based on their professional experience. Such expert judgments however may not be biasfree. De-biasing techniques therefore have been proposed in the software
Length bias correction in gene ontology enrichment analysis using logistic regression.
Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H
When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.
On the Upward Bias of the Dissimilarity Index and Its Corrections
Mazza, Angelo; Punzo, Antonio
The dissimilarity index of Duncan and Duncan is widely used in a broad range of contexts to assess the overall extent of segregation in the allocation of two groups in two or more units. Its sensitivity to random allocation implies an upward bias with respect to the unknown amount of systematic segregation. In this article, following a multinomial…
Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling
Chen, Huili; Liang, Qiuhua; Liu, Yong; Xie, Shuguang
Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM.
Application of bias factor method using random sampling technique for prediction accuracy improvement of critical eigenvalue of BWR
International Nuclear Information System (INIS)
Ito, Motohiro; Endo, Tomohiro; Yamamoto, Akio; Kuroda, Yusuke; Yoshii, Takashi
The bias factor method based on the random sampling technique is applied to the benchmark problem of Peach Bottom Unit 2. Validity and availability of the present method, i.e. correction of calculation results and reduction of uncertainty, are confirmed in addition to features and performance of the present method. In the present study, core characteristics in cycle 3 are corrected with the proposed method using predicted and 'measured' critical eigenvalues in cycles 1 and 2. As the source of uncertainty, variance-covariance of cross sections is considered. The calculation results indicate that bias between predicted and measured results, and uncertainty owing to cross section can be reduced. Extension to other uncertainties such as thermal hydraulics properties will be a future task. (author)
Sources of method bias in social science research and recommendations on how to control it.
Podsakoff, Philip M; MacKenzie, Scott B; Podsakoff, Nathan P
Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
Mechanism for and method of biasing magnetic sensor
Kautz, David R.
A magnetic sensor package having a biasing mechanism involving a coil-generated, resistor-controlled magnetic field for providing a desired biasing effect. In a preferred illustrated embodiment, the package broadly comprises a substrate; a magnetic sensor element; a biasing mechanism, including a coil and a first resistance element; an amplification mechanism; a filter capacitor element; and an encapsulant. The sensor is positioned within the coil. A current applied to the coil produces a biasing magnetic field. The biasing magnetic field is controlled by selecting a resistance value for the first resistance element which achieves the desired biasing effect. The first resistance element preferably includes a plurality of selectable resistors, the selection of one or more of which sets the resistance value.
An interactive website for analytical method comparison and bias estimation.
Bahar, Burak; Tuncel, Ayse F; Holmes, Earle W; Holmes, Daniel T
Regulatory standards mandate laboratories to perform studies to ensure accuracy and reliability of their test results. Method comparison and bias estimation are important components of these studies. We developed an interactive website for evaluating the relative performance of two analytical methods using R programming language tools. The website can be accessed at https://bahar.shinyapps.io/method_compare/. The site has an easy-to-use interface that allows both copy-pasting and manual entry of data. It also allows selection of a regression model and creation of regression and difference plots. Available regression models include Ordinary Least Squares, Weighted-Ordinary Least Squares, Deming, Weighted-Deming, Passing-Bablok and Passing-Bablok for large datasets. The server processes the data and generates downloadable reports in PDF or HTML format. Our website provides clinical laboratories a practical way to assess the relative performance of two analytical methods. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.
Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application
Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang
The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias c...
Development and Application of Tools for MRI Analysis - A Study on the Effects of Exercise in Patients with Alzheimer's Disease and Generative Models for Bias Field Correction in MR Brain Imaging
DEFF Research Database (Denmark)
Larsen, Christian Thode
in several cognitive performance measures, including mental speed, attention and verbal uency. MRI suffers from an image artifact often referred to as the "bias field". This effect complicates automatized analysis of the images. For this reason, bias field correction is typical an early preprocessing step...... as a "histogram sharpening" method, actually employs an underlying generative model, and that the bias field is estimated using an algorithm that is identical to generalized expectation maximization, but relies on heuristic parameter updates. The thesis progresses to present a new generative model...
Bias correction of daily precipitation projected by the CORDEX-Africa ensemble for a sparsely gauged region in West Africa with regionalized distribution parameters
Lorenz, Manuel; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald
Reliable estimates of future climatic conditions are indispensable for the sustainable planning of agricultural activities in West Africa. Precipitation time series of regional climate models (RCMs) typically exhibit a bias in the distribution of both rainfall intensities and wet day frequencies. Furthermore, the annual and monthly sums of precipitation may remarkably vary from the observations in this region. As West Africa experiences a distinct rainy season, sowing dates are oftentimes planned based on the beginning of this rainfall period. A biased representation of the annual cycle of precipitation in the uncorrected RCMs can therefore lead to crop failure. The precipitation ensemble, obtained from the Coordinated Downscaling Experiment CORDEX-Africa, was bias-corrected for the study region in West Africa (extending approximately 343,358 km2) which covers large parts of Burkina Faso, Ghana and Benin. In oder to debias the RCM precipitation simulations, a Quantile-Mapping method was applied to the historical period 1950-2005. For the RCM future projections (2006-2100), the Double-Quantile-Mapping procedure was chosen. This method makes use of the shift in the distribution function of the future precipitation values which allows to incorporate the climate change signal of the RCM projections into the bias correction. As large areas of the study region are ungauged, the assignment of the information from the nearest station to the ungauged location would lead to sharp changes in the estimated statistics from one location to another. Thus, the distribution parameters needed for the Quantile-Mapping were estimated by Kriging the distribution parameters of the available measurement stations. This way it is possible to obtain reasonable estimates of the expected distribution of precipitation at ungauged locations. The presentation will illustrate some aspects and trade-offs in the distribution parameter interpolation as well as an analysis of the uncertainties of the
Different partial volume correction methods lead to different conclusions
DEFF Research Database (Denmark)
Greve, Douglas N; Salat, David H; Bowen, Spencer L
A cross-sectional group study of the effects of aging on brain metabolism as measured with (18)F-FDG-PET was performed using several different partial volume correction (PVC) methods: no correction (NoPVC), Meltzer (MZ), Müller-Gärtner (MG), and the symmetric geometric transfer matrix (SGTM) usin...
Simulating water hammer with corrective smoothed particle method
Hou, Q.; Kruisbrink, A.C.H.; Tijsseling, A.S.; Keramat, A.
The corrective smoothed particle method (CSPM) is used to simulate water hammer. The spatial derivatives in the water-hammer equations are approximated by a corrective kernel estimate. For the temporal derivatives, the Euler-forward time integration algorithm is employed. The CSPM results are in
Method of absorbance correction in a spectroscopic heating value sensor
Saveliev, Alexei; Jangale, Vilas Vyankatrao; Zelepouga, Sergeui; Pratapas, John
A method and apparatus for absorbance correction in a spectroscopic heating value sensor in which a reference light intensity measurement is made on a non-absorbing reference fluid, a light intensity measurement is made on a sample fluid, and a measured light absorbance of the sample fluid is determined. A corrective light intensity measurement at a non-absorbing wavelength of the sample fluid is made on the sample fluid from which an absorbance correction factor is determined. The absorbance correction factor is then applied to the measured light absorbance of the sample fluid to arrive at a true or accurate absorbance for the sample fluid.
Occupational noise exposure and age correction: the problem of selection bias.
Dobie, Robert A
Selection bias often invalidates conclusions about populations based on clinical convenience samples. A recent paper in this journal makes two surprising assertions about noise-induced permanent threshold shift (NIPTS): first, that there is more NIPTS at 2 kHz than at higher frequencies; second, that NIPTS declines with advancing age. Neither assertion can be supported with the data presented, which were obtained from a clinical sample; both are consistent with the hypothesis that people who choose to attend an audiology clinic have worse hearing, especially at 2 kHz, than people of the same age and gender who choose not to attend.
A spectrum correction method for fuel assembly rehomogenization
International Nuclear Information System (INIS)
Lee, Kyung Taek; Cho, Nam Zin
To overcome the limitation of existing homogenization methods based on the single assembly calculation with zero current boundary condition, we propose a new rehomogenization method, named spectrum correction method (SCM), consisting of the multigroup energy spectrum approximation by spectrum correction and the condensed two-group heterogeneous single assembly calculations with non-zero current boundary condition. In SCM, the spectrum shifting phenomena caused by current across assembly interfaces are considered by the spectrum correction at group condensation stage at first. Then, heterogeneous single assembly calculations with two-group cross sections condensed by using corrected multigroup energy spectrum are performed to obtain rehomogenized nodal diffusion parameters, i.e., assembly-wise homogenized cross sections and discontinuity factors. To evaluate the performance of SCM, it was applied to the analytic function expansion nodal (AFEN) method and several test problems were solved. The results show that SCM can reduce the errors significantly both in multiplication factors and assembly averaged power distributions
Methods to Increase Educational Effectiveness in an Adult Correctional Setting.
Kuster, Byron
A correctional educator reflects on methods that improve instructional effectiveness. These include teacher-student collaboration, clear goals, student accountability, positive classroom atmosphere, high expectations, and mutual respect. (SK)
Automated general temperature correction method for dielectric soil moisture sensors
Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao
An effective temperature correction method for dielectric sensors is important to ensure the accuracy of soil water content (SWC) measurements of local to regional-scale soil moisture monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at soil moisture monitoring networks with different sensor setups and those that cover diverse climatic conditions and soil types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and soil type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International Soil Moisture Monitoring Network and another nine stations from a local soil moisture monitoring network in Mongolia. Soil moisture monitoring networks used in this study cover four major climates and six major soil types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a
Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing
Malakar, Nabin K.; Lary, D. L.; Moore, A.; Gencaga, D.; Roscoe, B.; Albayrak, Arif; Petrenko, Maksym; Wei, Jennifer
Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.
A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix.
Westgate, Philip M
Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference. Copyright © 2012 John Wiley & Sons, Ltd.
DEFF Research Database (Denmark)
Pinkevych, Mykola; Cromer, Deborah; Tolstrup, Martin
[This corrects the article DOI: 10.1371/journal.ppat.1005000.][This corrects the article DOI: 10.1371/journal.ppat.1005740.][This corrects the article DOI: 10.1371/journal.ppat.1005679.].......[This corrects the article DOI: 10.1371/journal.ppat.1005000.][This corrects the article DOI: 10.1371/journal.ppat.1005740.][This corrects the article DOI: 10.1371/journal.ppat.1005679.]....
Metric-based method of software requirements correctness improvement
Directory of Open Access Journals (Sweden)
Yaremchuk Svitlana
Full Text Available The work highlights the most important principles of software reliability management (SRM. The SRM concept construes a basis for developing a method of requirements correctness improvement. The method assumes that complicated requirements contain more actual and potential design faults/defects. The method applies a newer metric to evaluate the requirements complexity and double sorting technique evaluating the priority and complexity of a particular requirement. The method enables to improve requirements correctness due to identification of a higher number of defects with restricted resources. Practical application of the proposed method in the course of demands review assured a sensible technical and economic effect.
[Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance].
Zhang, Haixia; Zhao, Junkang; Gu, Caijiao; Cui, Yan; Rong, Huiying; Meng, Fanlong; Wang, Tong
The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance (URBMI) in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were returned by the students (or their parents) selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the returned questionnaires, 2.52% existed not missing at random (NMAR) of dependent variable and 7.14% existed missing at random (MAR) of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching (PMM) method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.
Experimental aspects of buoyancy correction in measuring reliable highpressure excess adsorption isotherms using the gravimetric method.
Nguyen, Huong Giang T; Horn, Jarod C; Thommes, Matthias; van Zee, Roger D; Espinal, Laura
Addressing reproducibility issues in adsorption measurements is critical to accelerating the path to discovery of new industrial adsorbents and to understanding adsorption processes. A National Institute of Standards and Technology Reference Material, RM 8852 (ammonium ZSM-5 zeolite), and two gravimetric instruments with asymmetric two-beam balances were used to measure high-pressure adsorption isotherms. This work demonstrates how common approaches to buoyancy correction, a key factor in obtaining the mass change due to surface excess gas uptake from the apparent mass change, can impact the adsorption isotherm data. Three different approaches to buoyancy correction were investigated and applied to the subcritical CO 2 and supercritical N 2 adsorption isotherms at 293 K. It was observed that measuring a collective volume for all balance components for the buoyancy correction (helium method) introduces an inherent bias in temperature partition when there is a temperature gradient (i.e. analysis temperature is not equal to instrument air bath temperature). We demonstrate that a blank subtraction is effective in mitigating the biases associated with temperature partitioning, instrument calibration, and the determined volumes of the balance components. In general, the manual and subtraction methods allow for better treatment of the temperature gradient during buoyancy correction. From the study, best practices specific to asymmetric two-beam balances and more general recommendations for measuring isotherms far from critical temperatures using gravimetric instruments are offered.
Local defect correction for boundary integral equation methods
Kakuba, G.; Anthonissen, M.J.H.
This paper presents a new approach to gridding for problems with localised regions of high activity. The technique of local defect correction has been studied for other methods as ¿nite difference methods and ¿nite volume methods. In this paper we develop the technique for the boundary element
Internal correction of spectral interferences and mass bias for selenium metabolism studies using enriched stable isotopes in combination with multiple linear regression.
Lunøe, Kristoffer; Martínez-Sierra, Justo Giner; Gammelgaard, Bente; Alonso, J Ignacio García
The analytical methodology for the in vivo study of selenium metabolism using two enriched selenium isotopes has been modified, allowing for the internal correction of spectral interferences and mass bias both for total selenium and speciation analysis. The method is based on the combination of an already described dual-isotope procedure with a new data treatment strategy based on multiple linear regression. A metabolic enriched isotope ((77)Se) is given orally to the test subject and a second isotope ((74)Se) is employed for quantification. In our approach, all possible polyatomic interferences occurring in the measurement of the isotope composition of selenium by collision cell quadrupole ICP-MS are taken into account and their relative contribution calculated by multiple linear regression after minimisation of the residuals. As a result, all spectral interferences and mass bias are corrected internally allowing the fast and independent quantification of natural abundance selenium ((nat)Se) and enriched (77)Se. In this sense, the calculation of the tracer/tracee ratio in each sample is straightforward. The method has been applied to study the time-related tissue incorporation of (77)Se in male Wistar rats while maintaining the (nat)Se steady-state conditions. Additionally, metabolically relevant information such as selenoprotein synthesis and selenium elimination in urine could be studied using the proposed methodology. In this case, serum proteins were separated by affinity chromatography while reverse phase was employed for urine metabolites. In both cases, (74)Se was used as a post-column isotope dilution spike. The application of multiple linear regression to the whole chromatogram allowed us to calculate the contribution of bromine hydride, selenium hydride, argon polyatomics and mass bias on the observed selenium isotope patterns. By minimising the square sum of residuals for the whole chromatogram, internal correction of spectral interferences and mass
Relative equilibrium plot improves graphical analysis and allows bias correction of standardized uptake value ratio in quantitative 11C-PiB PET studies.
Zhou, Yun; Sojkova, Jitka; Resnick, Susan M; Wong, Dean F
Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVRs) in ligand-receptor dynamic PET studies. The objective of this study was to use a recently developed relative equilibrium-based graphical (RE) plot method to improve and simplify the 2 commonly used methods for quantification of (11)C-Pittsburgh compound B ((11)C-PiB) PET. The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight (11)C-PiB dynamic PET scans (66 from controls and 12 from participants with mild cognitive impaired [MCI] from the Baltimore Longitudinal Study of Aging) were acquired over 90 min. Regions of interest (ROIs) were defined on coregistered MR images. Both the ROI and the pixelwise time-activity curves were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI time-activity curves were used as a reference for comparison of DVR estimates. Results from the theoretic analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI time-activity curves. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, and cingulate regions and the striatum were underestimated by the Logan plot (controls, 4%-12%; MCI, 9%-16%) and overestimated by the SUVR (controls, 8%-16%; MCI, 16%-24%). This bias was higher in the MCI group than in controls (P bias and higher consistency of DVR estimates than of SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of (11)C-PiB studies.
A method of detector correction for cosmic ray muon radiography
International Nuclear Information System (INIS)
Liu Yuanyuan; Zhao Ziran; Chen Zhiqiang; Zhang Li; Wang Zhentian
Cosmic ray muon radiography which has good penetrability and sensitivity to high-Z materials is an effective way for detecting shielded nuclear materials. The problem of data correction is one of the key points of muon radiography technique. Because of the influence of environmental background, environmental yawp and error of detectors, the raw data can not be used directly. If we used the raw data as the usable data to reconstruct without any corrections, it would turn up terrible artifacts. Based on the characteristics of the muon radiography system, aimed at the error of detectors, this paper proposes a method of detector correction. The simulation experiments demonstrate that this method can effectively correct the error produced by detectors. Therefore, we can say that it does a further step to let the technique of cosmic muon radiography into out real life. (authors)
Off-nadir antenna bias correction using Amazon rain forest sigma deg data. [Brazil
Birrer, I. J.; Bracalente, E. M.; Dome, G. J.; Sweet, J.; Berthold, G.; Moore, R. K. (Principal Investigator)
The radar response from the Amazon rain forest was studied to determine the suitability of this region for use as a standard target to calibrate a scatterometer like that proposed for the National Ocean Satellite System (NOSS). Backscattering observations made by the SEASAT-1 scatterometer system show the Amazon rain forest to be a homogeneous, azimuthally-isotropic, radar target which is insensitive to polarization. The variation with angle of incidence may be adequately modeled as sigma deg (dB) = alpha theta + beta with typical values for the incidence-angle coefficient from 0.07 dB deg to 0.15 dB/deg. A small diurnal effect occurs, with measurements at sunrise being 0.5 dB to 1 dB higher than the rest of the day. Maximum likelihood estimation algorithms are presented which permit determination of relative bias and true pointing angle for each beam. Specific implementation of these algorithms for the proposed NOSS scatterometer system is also discussed.
Off-nadir antenna bias correction using Amazon rain sigma(0) data
Birrer, I. J.; Dome, G. J.; Sweet, J.; Berthold, G.; Moore, R. K.
The radar response from the Amazon rain forest was studied to determine the suitability of this region for use as a standard target to calibrate a scatterometer like that proposed for the National Oceanic Satellite System (NOSS). Backscattering observations made by the SEASAT Scatterometer System (SASS) showed the Amazon rain forest to be a homogeneous, azimuthally-isotropic, radar target which was insensitive to polarization. The variation with angle of incidence was adequately modeled as scattering coefficient (dB) = a theta b with typical values for the incidence-angle coefficient from 0.07 to 0.15 dB/deg. A small diurnal effect occurs, with measurements at sunrise being 0.5 dB to 1 dB higher than the rest of the day. Maximum-likelihood estimation algorithms presented here permit determination of relative bias and true pointing angle for each beam. Specific implementation of these algorithms for the proposed NOSS scatterometer system is also discussed.
An corrective method to correct of the inherent flaw of the asynchronization direct counting circuit
International Nuclear Information System (INIS)
Wang Renfei; Liu Congzhan; Jin Yongjie; Zhang Zhi; Li Yanguo
As a inherent flaw of the Asynchronization Direct Counting Circuit, the crosstalk, which is resulted from the randomicity of the time-signal always exists between two adjacent channels. In order to reduce the counting error derived from the crosstalk, the author propose an effective method to correct the flaw after analysing the mechanism of the crosstalk
Phylogenetic uncertainty can bias the number of evolutionary transitions estimated from ancestral state reconstruction methods.
Duchêne, Sebastian; Lanfear, Robert
Ancestral state reconstruction (ASR) is a popular method for exploring the evolutionary history of traits that leave little or no trace in the fossil record. For example, it has been used to test hypotheses about the number of evolutionary origins of key life-history traits such as oviparity, or key morphological structures such as wings. Many studies that use ASR have suggested that the number of evolutionary origins of such traits is higher than was previously thought. The scope of such inferences is increasing rapidly, facilitated by the construction of very large phylogenies and life-history databases. In this paper, we use simulations to show that the number of evolutionary origins of a trait tends to be overestimated when the phylogeny is not perfect. In some cases, the estimated number of transitions can be several fold higher than the true value. Furthermore, we show that the bias is not always corrected by standard approaches to account for phylogenetic uncertainty, such as repeating the analysis on a large collection of possible trees. These findings have important implications for studies that seek to estimate the number of origins of a trait, particularly those that use large phylogenies that are associated with considerable uncertainty. We discuss the implications of this bias, and methods to ameliorate it. © 2015 Wiley Periodicals, Inc.
Implementation of the Centroid Method for the Correction of Turbulence
Directory of Open Access Journals (Sweden)
Enric Meinhardt-Llopis
Full Text Available The centroid method for the correction of turbulence consists in computing the Karcher-Fréchet mean of the sequence of input images. The direction of deformation between a pair of images is determined by the optical flow. A distinguishing feature of the centroid method is that it can produce useful results from an arbitrarily small set of input images.
[Study on phase correction method of spatial heterodyne spectrometer].
Wang, Xin-Qiang; Ye, Song; Zhang, Li-Juan; Xiong, Wei
Phase distortion exists in collected interferogram because of a variety of measure reasons when spatial heterodyne spectrometers are used in practice. So an improved phase correction method is presented. The phase curve of interferogram was obtained through Fourier inverse transform to extract single side transform spectrum, based on which, the phase distortions were attained by fitting phase slope, so were the phase correction functions, and the convolution was processed between transform spectrum and phase correction function to implement spectrum phase correction. The method was applied to phase correction of actually measured monochromatic spectrum and emulational water vapor spectrum. Experimental results show that the low-frequency false signals in monochromatic spectrum fringe would be eliminated effectively to increase the periodicity and the symmetry of interferogram, in addition when the continuous spectrum imposed phase error was corrected, the standard deviation between it and the original spectrum would be reduced form 0.47 to 0.20, and thus the accuracy of spectrum could be improved.
The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data.
Regier, Michael D; Moodie, Erica E M
We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.
An attenuation correction method for PET/CT images
International Nuclear Information System (INIS)
Ue, Hidenori; Yamazaki, Tomohiro; Haneishi, Hideaki
In PET/CT systems, accurate attenuation correction can be achieved by creating an attenuation map from an X-ray CT image. On the other hand, respiratory-gated PET acquisition is an effective method for avoiding motion blurring of the thoracic and abdominal organs caused by respiratory motion. In PET/CT systems employing respiratory-gated PET, using an X-ray CT image acquired during breath-holding for attenuation correction may have a large effect on the voxel values, especially in regions with substantial respiratory motion. In this report, we propose an attenuation correction method in which, as the first step, a set of respiratory-gated PET images is reconstructed without attenuation correction, as the second step, the motion of each phase PET image from the PET image in the same phase as the CT acquisition timing is estimated by the previously proposed method, as the third step, the CT image corresponding to each respiratory phase is generated from the original CT image by deformation according to the motion vector maps, and as the final step, attenuation correction using these CT images and reconstruction are performed. The effectiveness of the proposed method was evaluated using 4D-NCAT phantoms, and good stability of the voxel values near the diaphragm was observed. (author)
An Automated Baseline Correction Method Based on Iterative Morphological Operations.
Chen, Yunliang; Dai, Liankui
Raman spectra usually suffer from baseline drift caused by fluorescence or other reasons. Therefore, baseline correction is a necessary and crucial step that must be performed before subsequent processing and analysis of Raman spectra. An automated baseline correction method based on iterative morphological operations is proposed in this work. The method can adaptively determine the structuring element first and then gradually remove the spectral peaks during iteration to get an estimated baseline. Experiments on simulated data and real-world Raman data show that the proposed method is accurate, fast, and flexible for handling different kinds of baselines in various practical situations. The comparison of the proposed method with some state-of-the-art baseline correction methods demonstrates its advantages over the existing methods in terms of accuracy, adaptability, and flexibility. Although only Raman spectra are investigated in this paper, the proposed method is hopefully to be used for the baseline correction of other analytical instrumental signals, such as IR spectra and chromatograms.
The various correction methods to the high precision aeromagnetic data
International Nuclear Information System (INIS)
Xu Guocang; Zhu Lin; Ning Yuanli; Meng Xiangbao; Zhang Hongjian
In the airborne geophysical survey, an outstanding achievement first depends on the measurement precision of the instrument, and the choice of measurement conditions, the reliability of data collection, followed by the correct method of measurement data processing, the rationality of the data interpretation. Obviously, geophysical data processing is an important task for the comprehensive interpretation of the measurement results, processing method is correct or not directly related to the quality of the final results. we have developed a set of personal computer software to aeromagnetic and radiometric survey data processing in the process of actual production and scientific research in recent years, and successfully applied to the production. The processing methods and flowcharts to the high precision aromagnetic data were simply introduced in this paper. However, the mathematical techniques of the various correction programes to IGRF and flying height and magnetic diurnal variation were stressily discussed in the paper. Their processing effectness were illustrated by taking an example as well. (authors)
Method for introducing bias magnetization in ungaped cores
DEFF Research Database (Denmark)
Aguilar, Andres Revilla; Munk-Nielsen, Stig
The use of permanent magnets for bias magnetization is a known technique to increase the energy storage capability in DC inductors, resulting in a size reduction or increased current rating. This paper presents a brief introduction on the different permanent magnet inductor's configurations found...
Manual Optical Attitude Re-initialization of a Crew Vehicle in Space Using Bias Corrected Gyro Data
Gioia, Christopher J.
NASA and other space agencies have shown interest in sending humans on missions beyond low Earth orbit. Proposed is an algorithm that estimates the attitude of a manned spacecraft using measured line-of-sight (LOS) vectors to stars and gyroscope measurements. The Manual Optical Attitude Reinitialization (MOAR) algorithm and corresponding device draw inspiration from existing technology from the Gemini, Apollo and Space Shuttle programs. The improvement over these devices is the capability of estimating gyro bias completely independent from re-initializing attitude. It may be applied to the lost-in-space problem, where the spacecraft's attitude is unknown. In this work, a model was constructed that simulated gyro data using the Farrenkopf gyro model, and LOS measurements from a spotting scope were then computed from it. Using these simulated measurements, gyro bias was estimated by comparing measured interior star angles to those derived from a star catalog and then minimizing the difference using an optimization technique. Several optimization techniques were analyzed, and it was determined that the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm performed the best when combined with a grid search technique. Once estimated, the gyro bias was removed and attitude was determined by solving the Wahba Problem via the Singular Value Decomposition (SVD) approach. Several Monte Carlo simulations were performed that looked at different operating conditions for the MOAR algorithm. These included the effects of bias instability, using different constellations for data collection, sampling star measurements in different orders, and varying the time between measurements. A common method of estimating gyro bias and attitude in a Multiplicative Extended Kalman Filter (MEKF) was also explored and disproven for use in the MOAR algorithm. A prototype was also constructed to validate the proposed concepts. It was built using a simple spotting scope, MEMS grade IMU, and a Raspberry
A vibration correction method for free-fall absolute gravimeters
Qian, J.; Wang, G.; Wu, K.; Wang, L. J.
An accurate determination of gravitational acceleration, usually approximated as 9.8 m s-2, has been playing an important role in the areas of metrology, geophysics, and geodetics. Absolute gravimetry has been experiencing rapid developments in recent years. Most absolute gravimeters today employ a free-fall method to measure gravitational acceleration. Noise from ground vibration has become one of the most serious factors limiting measurement precision. Compared to vibration isolators, the vibration correction method is a simple and feasible way to reduce the influence of ground vibrations. A modified vibration correction method is proposed and demonstrated. A two-dimensional golden section search algorithm is used to search for the best parameters of the hypothetical transfer function. Experiments using a T-1 absolute gravimeter are performed. It is verified that for an identical group of drop data, the modified method proposed in this paper can achieve better correction effects with much less computation than previous methods. Compared to vibration isolators, the correction method applies to more hostile environments and even dynamic platforms, and is expected to be used in a wider range of applications.
Non-stationary Bias Correction of Monthly CMIP5 Temperature Projections over China using a Residual-based Bagging Tree Model
Yang, T.; Lee, C.
The biases in the Global Circulation Models (GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed Residual-based Bagging Tree (RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modeling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared to the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44 ºC, 2.59 ºC, and 4.71 ºC by the end of the century, respectively, when compared to the average temperature during 1970 - 1999.
A Horizontal Tilt Correction Method for Ship License Numbers Recognition
Liu, Baolong; Zhang, Sanyuan; Hong, Zhenjie; Ye, Xiuzi
An automatic ship license numbers (SLNs) recognition system plays a significant role in intelligent waterway transportation systems since it can be used to identify ships by recognizing the characters in SLNs. Tilt occurs frequently in many SLNs because the monitors and the ships usually have great vertical or horizontal angles, which decreases the accuracy and robustness of a SLNs recognition system significantly. In this paper, we present a horizontal tilt correction method for SLNs. For an input tilt SLN image, the proposed method accomplishes the correction task through three main steps. First, a MSER-based characters' center-points computation algorithm is designed to compute the accurate center-points of the characters contained in the input SLN image. Second, a L 1- L 2 distance-based straight line is fitted to the computed center-points using M-estimator algorithm. The tilt angle is estimated at this stage. Finally, based on the computed tilt angle, an affine transformation rotation is conducted to rotate and to correct the input SLN horizontally. At last, the proposed method is tested on 200 tilt SLN images, the proposed method is proved to be effective with a tilt correction rate of 80.5%.
Correction of measured multiplicity distributions by the simulated annealing method
International Nuclear Information System (INIS)
Hafidouni, M.
Simulated annealing is a method used to solve combinatorial optimization problems. It is used here for the correction of the observed multiplicity distribution from S-Pb collisions at 200 GeV/c per nucleon. (author) 11 refs., 2 figs
A Hold-out method to correct PCA variance inflation
DEFF Research Database (Denmark)
Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Hansen, Lars Kai
In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure...
A simulation study to compare three self-controlled case series approaches: correction for violation of assumption and evaluation of bias.
Hua, Wei; Sun, Guoying; Dodd, Caitlin N; Romio, Silvana A; Whitaker, Heather J; Izurieta, Hector S; Black, Steven; Sturkenboom, Miriam C J M; Davis, Robert L; Deceuninck, Genevieve; Andrews, N J
The assumption that the occurrence of outcome event must not alter subsequent exposure probability is critical for preserving the validity of the self-controlled case series (SCCS) method. This assumption is violated in scenarios in which the event constitutes a contraindication for exposure. In this simulation study, we compared the performance of the standard SCCS approach and two alternative approaches when the event-independent exposure assumption was violated. Using the 2009 H1N1 and seasonal influenza vaccines and Guillain-Barré syndrome as a model, we simulated a scenario in which an individual may encounter multiple unordered exposures and each exposure may be contraindicated by the occurrence of outcome event. The degree of contraindication was varied at 0%, 50%, and 100%. The first alternative approach used only cases occurring after exposure with follow-up time starting from exposure. The second used a pseudo-likelihood method. When the event-independent exposure assumption was satisfied, the standard SCCS approach produced nearly unbiased relative incidence estimates. When this assumption was partially or completely violated, two alternative SCCS approaches could be used. While the post-exposure cases only approach could handle only one exposure, the pseudo-likelihood approach was able to correct bias for both exposures. Violation of the event-independent exposure assumption leads to an overestimation of relative incidence which could be corrected by alternative SCCS approaches. In multiple exposure situations, the pseudo-likelihood approach is optimal; the post-exposure cases only approach is limited in handling a second exposure and may introduce additional bias, thus should be used with caution. Copyright © 2013 John Wiley & Sons, Ltd.
Predictive Methods for Dense Polymer Networks: Combating Bias with Bio-Based Structures
Combating bias with bio - based structures 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Andrew J. Guenthner...unlimited. PA Clearance 16152 Integrity Service Excellence Predictive methods for dense polymer networks: Combating bias with bio -based...Architectural Bias • Comparison of Petroleum-Based and Bio -Based Chemical Architectures • Continuing Research on Structure-Property Relationships using
Correcting for cryptic relatedness by a regression-based genomic control method
Directory of Open Access Journals (Sweden)
Yang Yaning
Full Text Available Abstract Background Genomic control (GC method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction. Results In this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method. Conclusion The proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure.
Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.
Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul
Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.
Method for decoupling error correction from privacy amplification
Energy Technology Data Exchange (ETDEWEB)
Lo, Hoi-Kwong [Department of Electrical and Computer Engineering and Department of Physics, University of Toronto, 10 King' s College Road, Toronto, Ontario, Canada, M5S 3G4 (Canada)
In a standard quantum key distribution (QKD) scheme such as BB84, two procedures, error correction and privacy amplification, are applied to extract a final secure key from a raw key generated from quantum transmission. To simplify the study of protocols, it is commonly assumed that the two procedures can be decoupled from each other. While such a decoupling assumption may be valid for individual attacks, it is actually unproven in the context of ultimate or unconditional security, which is the Holy Grail of quantum cryptography. In particular, this means that the application of standard efficient two-way error-correction protocols like Cascade is not proven to be unconditionally secure. Here, I provide the first proof of such a decoupling principle in the context of unconditional security. The method requires Alice and Bob to share some initial secret string and use it to encrypt their communications in the error correction stage using one-time-pad encryption. Consequently, I prove the unconditional security of the interactive Cascade protocol proposed by Brassard and Salvail for error correction and modified by one-time-pad encryption of the error syndrome, followed by the random matrix protocol for privacy amplification. This is an efficient protocol in terms of both computational power and key generation rate. My proof uses the entanglement purification approach to security proofs of QKD. The proof applies to all adaptive symmetric methods for error correction, which cover all existing methods proposed for BB84. In terms of the net key generation rate, the new method is as efficient as the standard Shor-Preskill proof.
Correcting saturation of detectors for particle/droplet imaging methods
International Nuclear Information System (INIS)
Kalt, Peter A M
Laser-based diagnostic methods are being applied to more and more flows of theoretical and practical interest and are revealing interesting new flow features. Imaging particles or droplets in nephelometry and laser sheet dropsizing methods requires a trade-off of maximized signal-to-noise ratio without over-saturating the detector. Droplet and particle imaging results in lognormal distribution of pixel intensities. It is possible to fit a derived lognormal distribution to the histogram of measured pixel intensities. If pixel intensities are clipped at a saturated value, it is possible to estimate a presumed probability density function (pdf) shape without the effects of saturation from the lognormal fit to the unsaturated histogram. Information about presumed shapes of the pixel intensity pdf is used to generate corrections that can be applied to data to account for saturation. The effects of even slight saturation are shown to be a significant source of error on the derived average. The influence of saturation on the derived root mean square (rms) is even more pronounced. It is found that errors on the determined average exceed 5% when the number of saturated samples exceeds 3% of the total. Errors on the rms are 20% for a similar saturation level. This study also attempts to delineate limits, within which the detector saturation can be accurately corrected. It is demonstrated that a simple method for reshaping the clipped part of the pixel intensity histogram makes accurate corrections to account for saturated pixels. These outcomes can be used to correct a saturated signal, quantify the effect of saturation on a derived average and offer a method to correct the derived average in the case of slight to moderate saturation of pixels
Method for decoupling error correction from privacy amplification
International Nuclear Information System (INIS)
Lo, Hoi-Kwong
In a standard quantum key distribution (QKD) scheme such as BB84, two procedures, error correction and privacy amplification, are applied to extract a final secure key from a raw key generated from quantum transmission. To simplify the study of protocols, it is commonly assumed that the two procedures can be decoupled from each other. While such a decoupling assumption may be valid for individual attacks, it is actually unproven in the context of ultimate or unconditional security, which is the Holy Grail of quantum cryptography. In particular, this means that the application of standard efficient two-way error-correction protocols like Cascade is not proven to be unconditionally secure. Here, I provide the first proof of such a decoupling principle in the context of unconditional security. The method requires Alice and Bob to share some initial secret string and use it to encrypt their communications in the error correction stage using one-time-pad encryption. Consequently, I prove the unconditional security of the interactive Cascade protocol proposed by Brassard and Salvail for error correction and modified by one-time-pad encryption of the error syndrome, followed by the random matrix protocol for privacy amplification. This is an efficient protocol in terms of both computational power and key generation rate. My proof uses the entanglement purification approach to security proofs of QKD. The proof applies to all adaptive symmetric methods for error correction, which cover all existing methods proposed for BB84. In terms of the net key generation rate, the new method is as efficient as the standard Shor-Preskill proof
An efficient dose-compensation method for proximity effect correction
International Nuclear Information System (INIS)
Wang Ying; Han Weihua; Yang Xiang; Zhang Yang; Yang Fuhua; Zhang Renping
A novel simple dose-compensation method is developed for proximity effect correction in electron-beam lithography. The sizes of exposed patterns depend on dose factors while other exposure parameters (including accelerate voltage, resist thickness, exposing step size, substrate material, and so on) remain constant. This method is based on two reasonable assumptions in the evaluation of the compensated dose factor: one is that the relation between dose factors and circle-diameters is linear in the range under consideration; the other is that the compensated dose factor is only affected by the nearest neighbors for simplicity. Four-layer-hexagon photonic crystal structures were fabricated as test patterns to demonstrate this method. Compared to the uncorrected structures, the homogeneity of the corrected hole-size in photonic crystal structures was clearly improved. (semiconductor technology)
Simple method for correct enumeration of Staphylococcus aureus
DEFF Research Database (Denmark)
Haaber, J.; Cohn, M. T.; Petersen, A.
culture. When grown in such liquid cultures, the human pathogen Staphylococcus aureus is characterized by its aggregation of single cells into clusters of variable size. Here, we show that aggregation during growth in the laboratory standard medium tryptic soy broth (TSB) is common among clinical...... and laboratory S. aureus isolates and that aggregation may introduce significant bias when applying standard enumeration methods on S. aureus growing in laboratory batch cultures. We provide a simple and efficient sonication procedure, which can be applied prior to optical density measurements to give...
Relative equilibrium plot improves graphical analysis and allows bias correction of SUVR in quantitative [11C]PiB PET studies
Zhou, Yun; Sojkova, Jitka; Resnick, Susan M.; Wong, Dean F.
Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVR) in ligand-receptor dynamic PET studies. The objective of this study is to use a recently developed relative equilibrium-based graphical plot (RE plot) method to improve and simplify the two commonly used methods for quantification of [11C]PiB PET. Methods The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight [11C]PiB dynamic PET scans (66 from controls and 12 from mildly cognitively impaired participants (MCI) from the Baltimore Longitudinal Study of Aging (BLSA)) were acquired over 90 minutes. Regions of interest (ROIs) were defined on coregistered MRIs. Both the ROI and pixelwise time activity curves (TACs) were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI TACs were used as a reference for comparison of DVR estimates. Results Results from the theoretical analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI TACs. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, cingulate regions, and the striatum were underestimated by the Logan plot (controls 4 – 12%; MCI 9 – 16%) and overestimated by the SUVR (controls 8 – 16%; MCI 16 – 24%). This bias was higher in the MCI group than in controls (p plot or the bcSUVR. Conclusion The RE plot improves pixel-wise quantification of [11C]PiB dynamic PET compared to the conventional Logan plot. The bcSUVR results in lower bias and higher consistency of DVR estimates compared to SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of [11C]PiB studies. PMID:22414634
A rigid motion correction method for helical computed tomography (CT)
International Nuclear Information System (INIS)
Kim, J-H; Kyme, A; Fulton, R; Nuyts, J; Kuncic, Z
We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker sampling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For example it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data. (paper)
A new method to measure galaxy bias by combining the density and weak lensing fields
Energy Technology Data Exchange (ETDEWEB)
Pujol, Arnau; Chang, Chihway; Gaztañaga, Enrique; Amara, Adam; Refregier, Alexandre; Bacon, David J.; Carretero, Jorge; Castander, Francisco J.; Crocce, Martin; Fosalba, Pablo; Manera, Marc; Vikram, Vinu
We present a new method to measure redshift-dependent galaxy bias by combining information from the galaxy density field and the weak lensing field. This method is based on the work of Amara et al., who use the galaxy density field to construct a bias-weighted convergence field κg. The main difference between Amara et al.'s work and our new implementation is that here we present another way to measure galaxy bias, using tomography instead of bias parametrizations. The correlation between κg and the true lensing field κ allows us to measure galaxy bias using different zero-lag correlations, such as <κgκ>/<κκ> or <κgκg>/<κgκ>. Our method measures the linear bias factor on linear scales, under the assumption of no stochasticity between galaxies and matter. We use the Marenostrum Institut de Ciències de l'Espai (MICE) simulation to measure the linear galaxy bias for a flux-limited sample (i < 22.5) in tomographic redshift bins using this method. This article is the first that studies the accuracy and systematic uncertainties associated with the implementation of the method and the regime in which it is consistent with the linear galaxy bias defined by projected two-point correlation functions (2PCF). We find that our method is consistent with a linear bias at the per cent level for scales larger than 30 arcmin, while non-linearities appear at smaller scales. This measurement is a good complement to other measurements of bias, since it does not depend strongly on σ8 as do the 2PCF measurements. We will apply this method to the Dark Energy Survey Science Verification data in a follow-up article.
Brain extraction in partial volumes T2*@7T by using a quasi-anatomic segmentation with bias field correction.
Valente, João; Vieira, Pedro M; Couto, Carlos; Lima, Carlos S
Poor brain extraction in Magnetic Resonance Imaging (MRI) has negative consequences in several types of brain post-extraction such as tissue segmentation and related statistical measures or pattern recognition algorithms. Current state of the art algorithms for brain extraction work on weighted T1 and T2, being not adequate for non-whole brain images such as the case of T2*FLASH@7T partial volumes. This paper proposes two new methods that work directly in T2*FLASH@7T partial volumes. The first is an improvement of the semi-automatic threshold-with-morphology approach adapted to incomplete volumes. The second method uses an improved version of a current implementation of the fuzzy c-means algorithm with bias correction for brain segmentation. Under high inhomogeneity conditions the performance of the first method degrades, requiring user intervention which is unacceptable. The second method performed well for all volumes, being entirely automatic. State of the art algorithms for brain extraction are mainly semi-automatic, requiring a correct initialization by the user and knowledge of the software. These methods can't deal with partial volumes and/or need information from atlas which is not available in T2*FLASH@7T. Also, combined volumes suffer from manipulations such as re-sampling which deteriorates significantly voxel intensity structures making segmentation tasks difficult. The proposed method can overcome all these difficulties, reaching good results for brain extraction using only T2*FLASH@7T volumes. The development of this work will lead to an improvement of automatic brain lesions segmentation in T2*FLASH@7T volumes, becoming more important when lesions such as cortical Multiple-Sclerosis need to be detected. Copyright © 2017 Elsevier B.V. All rights reserved.
bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests.
To Duc, Khanh
Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias-corrected inference tools are required. This paper introduce an R package, named bcROCsurface, which provides utility functions for verification bias-corrected ROC surface analysis. The shiny web application of the correction for verification bias in estimation of the ROC surface analysis is also developed. bcROCsurface may become an important tool for the statistical evaluation of three-class diagnostic markers in presence of verification bias. The R package, readme and example data are available on CRAN. The web interface enables users less familiar with R to evaluate the accuracy of diagnostic tests, and can be found at http://khanhtoduc.shinyapps.io/bcROCsurface_shiny/ .
Variance bias analysis for the Gelbard's batch method
Energy Technology Data Exchange (ETDEWEB)
Seo, Jae Uk; Shim, Hyung Jin [Seoul National Univ., Seoul (Korea, Republic of)
In this paper, variances and the bias will be derived analytically when the Gelbard's batch method is applied. And then, the real variance estimated from this bias will be compared with the real variance calculated from replicas. Variance and the bias were derived analytically when the batch method was applied. If the batch method was applied to calculate the sample variance, covariance terms between tallies which exist in the batch were eliminated from the bias. With the 2 by 2 fission matrix problem, we could calculate real variance regardless of whether or not the batch method was applied. However as batch size got larger, standard deviation of real variance was increased. When we perform a Monte Carlo estimation, we could get a sample variance as the statistical uncertainty of it. However, this value is smaller than the real variance of it because a sample variance is biased. To reduce this bias, Gelbard devised the method which is called the Gelbard's batch method. It has been certificated that a sample variance get closer to the real variance when the batch method is applied. In other words, the bias get reduced. This fact is well known to everyone in the MC field. However, so far, no one has given the analytical interpretation on it.
Bias lighting in a radiographic apparatus and method
International Nuclear Information System (INIS)
Mcbride, T.R.; Richey, J.B.
The radiographic system includes an x-ray source for irradiating a patient with x-radiation. An image intensifier receives the xradiation which has traversed the patient and produces an optical image of a radiation shadowgraph of the examined area of the patient. A television camera converts the optical image into a video signal. An image processor stores each frame of the video signal generated by the television camera as an electronic image of the optical image viewed on the image intensifier. Alternately , a plurality of frames from the television camera may be combined to produce a composite image. A bias light is provided adjacent the target of a television camera to illuminate the target before an optical image from the image intensifier is monitored by the television camera. This improves the linearity of the response of the television camera, particularly to low amplitude light intensities on the first few video frames generated by the camera
FFT swept filtering: a bias-free method for processing fringe signals in absolute gravimeters
Křen, Petr; Pálinkáš, Vojtech; Mašika, Pavel; Val'ko, Miloš
Absolute gravimeters, based on laser interferometry, are widely used for many applications in geoscience and metrology. Although currently the most accurate FG5 and FG5X gravimeters declare standard uncertainties at the level of 2-3 μGal, their inherent systematic errors affect the gravity reference determined by international key comparisons based predominately on the use of FG5-type instruments. The measurement results for FG5-215 and FG5X-251 clearly showed that the measured g-values depend on the size of the fringe signal and that this effect might be approximated by a linear regression with a slope of up to 0.030 μGal/mV . However, these empirical results do not enable one to identify the source of the effect or to determine a reasonable reference fringe level for correcting g-values in an absolute sense. Therefore, both gravimeters were equipped with new measuring systems (according to Křen et al. in Metrologia 53:27-40, 2016. https://doi.org/10.1088/0026-1394/53/1/27 applied for FG5), running in parallel with the original systems. The new systems use an analogue-to-digital converter HS5 to digitize the fringe signal and a new method of fringe signal analysis based on FFT swept bandpass filtering. We demonstrate that the source of the fringe size effect is connected to a distortion of the fringe signal due to the electronic components used in the FG5(X) gravimeters. To obtain a bias-free g-value, the FFT swept method should be applied for the determination of zero-crossings. A comparison of g-values obtained from the new and the original systems clearly shows that the original system might be biased by approximately 3-5 μGal due to improperly distorted fringe signal processing.
Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction
Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping
A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3
Correction of Misclassifications Using a Proximity-Based Estimation Method
Directory of Open Access Journals (Sweden)
Shmulevich Ilya
Full Text Available An estimation method for correcting misclassifications in signal and image processing is presented. The method is based on the use of context-based (temporal or spatial information in a sliding-window fashion. The classes can be purely nominal, that is, an ordering of the classes is not required. The method employs nonlinear operations based on class proximities defined by a proximity matrix. Two case studies are presented. In the first, the proposed method is applied to one-dimensional signals for processing data that are obtained by a musical key-finding algorithm. In the second, the estimation method is applied to two-dimensional signals for correction of misclassifications in images. In the first case study, the proximity matrix employed by the estimation method follows directly from music perception studies, whereas in the second case study, the optimal proximity matrix is obtained with genetic algorithms as the learning rule in a training-based optimization framework. Simulation results are presented in both case studies and the degree of improvement in classification accuracy that is obtained by the proposed method is assessed statistically using Kappa analysis.
Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion.
Tisdall, M Dylan; Reuter, Martin; Qureshi, Abid; Buckner, Randy L; Fischl, Bruce; van der Kouwe, André J W
Recent work has demonstrated that subject motion produces systematic biases in the metrics computed by widely used morphometry software packages, even when the motion is too small to produce noticeable image artifacts. In the common situation where the control population exhibits different behaviors in the scanner when compared to the experimental population, these systematic measurement biases may produce significant confounds for between-group analyses, leading to erroneous conclusions about group differences. While previous work has shown that prospective motion correction can improve perceived image quality, here we demonstrate that, in healthy subjects performing a variety of directed motions, the use of the volumetric navigator (vNav) prospective motion correction system significantly reduces the motion-induced bias and variance in morphometry. Copyright © 2015 Elsevier Inc. All rights reserved.
GPU accelerated manifold correction method for spinning compact binaries
Ran, Chong-xi; Liu, Song; Zhong, Shuang-ying
The graphics processing unit (GPU) acceleration of the manifold correction algorithm based on the compute unified device architecture (CUDA) technology is designed to simulate the dynamic evolution of the Post-Newtonian (PN) Hamiltonian formulation of spinning compact binaries. The feasibility and the efficiency of parallel computation on GPU have been confirmed by various numerical experiments. The numerical comparisons show that the accuracy on GPU execution of manifold corrections method has a good agreement with the execution of codes on merely central processing unit (CPU-based) method. The acceleration ability when the codes are implemented on GPU can increase enormously through the use of shared memory and register optimization techniques without additional hardware costs, implying that the speedup is nearly 13 times as compared with the codes executed on CPU for phase space scan (including 314 × 314 orbits). In addition, GPU-accelerated manifold correction method is used to numerically study how dynamics are affected by the spin-induced quadrupole-monopole interaction for black hole binary system.
Equation-Method for correcting clipping errors in OFDM signals.
Bibi, Nargis; Kleerekoper, Anthony; Muhammad, Nazeer; Cheetham, Barry
Orthogonal frequency division multiplexing (OFDM) is the digital modulation technique used by 4G and many other wireless communication systems. OFDM signals have significant amplitude fluctuations resulting in high peak to average power ratios which can make an OFDM transmitter susceptible to non-linear distortion produced by its high power amplifiers (HPA). A simple and popular solution to this problem is to clip the peaks before an OFDM signal is applied to the HPA but this causes in-band distortion and introduces bit-errors at the receiver. In this paper we discuss a novel technique, which we call the Equation-Method, for correcting these errors. The Equation-Method uses the Fast Fourier Transform to create a set of simultaneous equations which, when solved, return the amplitudes of the peaks before they were clipped. We show analytically and through simulations that this method can, correct all clipping errors over a wide range of clipping thresholds. We show that numerical instability can be avoided and new techniques are needed to enable the receiver to differentiate between correctly and incorrectly received frequency-domain constellation symbols.
Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations.
Hayes, Alison J; Clarke, Philip M; Lung, Tom Wc
Many studies have documented the bias in body mass index (BMI) determined from self-reported data on height and weight, but few have examined the change in bias over time. Using data from large, nationally-representative population health surveys, we examined change in bias in height and weight reporting among Australian adults between 1995 and 2008. Our study dataset included 9,635 men and women in 1995 and 9,141 in 2007-2008. We investigated the determinants of the bias and derived correction equations using 2007-2008 data, which can be applied when only self-reported anthropometric data are available. In 1995, self-reported BMI (derived from height and weight) was 1.2 units (men) and 1.4 units (women) lower than measured BMI. In 2007-2008, there was still underreporting, but the amount had declined to 0.6 units (men) and 0.7 units (women) below measured BMI. The major determinants of reporting error in 2007-2008 were age, sex, measured BMI, and education of the respondent. Correction equations for height and weight derived from 2007-2008 data and applied to self-reported data were able to adjust for the bias and were accurate across all age and sex strata. The diminishing reporting bias in BMI in Australia means that correction equations derived from 2007-2008 data may not be transferable to earlier self-reported data. Second, predictions of future overweight and obesity in Australia based on trends in self-reported information are likely to be inaccurate, as the change in reporting bias will affect the apparent increase in self-reported obesity prevalence.
Method for measuring multiple scattering corrections between liquid scintillators
Energy Technology Data Exchange (ETDEWEB)
Verbeke, J.M., E-mail: firstname.lastname@example.com; Glenn, A.M., E-mail: email@example.com; Keefer, G.J., E-mail: email@example.com; Wurtz, R.E., E-mail: firstname.lastname@example.com
A time-of-flight method is proposed to experimentally quantify the fractions of neutrons scattering between scintillators. An array of scintillators is characterized in terms of crosstalk with this method by measuring a californium source, for different neutron energy thresholds. The spectral information recorded by the scintillators can be used to estimate the fractions of neutrons multiple scattering. With the help of a correction to Feynman's point model theory to account for multiple scattering, these fractions can in turn improve the mass reconstruction of fissile materials under investigation.
A Method To ModifyCorrect The Performance Of Amplifiers
Directory of Open Access Journals (Sweden)
Rohith Krishnan R
Full Text Available Abstract The actual response of the amplifier may vary with the replacement of some aged or damaged components and this method is to compensate that problem. Here we use op-amp Fixator as the design tool. The tool helps us to isolate the selected circuit component from rest of the circuit adjust its operating point to correct the performance deviations and to modify the circuit without changing other parts of the circuit. A method to modifycorrect the performance of amplifiers by properly redesign the circuit is presented in this paper.
New method in obtaining correction factor of power confirming
International Nuclear Information System (INIS)
Deng Yongjun; Li Rundong; Liu Yongkang; Zhou Wei
Westcott theory is the most widely used method in reactor power calibration, which particularly suited to research reactor. But this method is very fussy because lots of correction parameters which rely on empirical formula to special reactor type are needed. The incidence coefficient between foil activity and reactor power was obtained by Monte-Carlo calculation, which was carried out with precise description of the reactor core and the foil arrangement position by MCNP input card. So the reactor power was determined by the core neutron fluence profile and the foil activity placed in the position for normalization use. The characteristic of this new method is simpler, more flexible and accurate than Westcott theory. In this paper, the results of SPRR-300 obtained by the new method in theory were compared with the experimental results, which verified the possibility of this new method. (authors)
A Method for Correcting IMRT Optimizer Heterogeneity Dose Calculations
International Nuclear Information System (INIS)
Zacarias, Albert S.; Brown, Mellonie F.; Mills, Michael D.
Radiation therapy treatment planning for volumes close to the patient's surface, in lung tissue and in the head and neck region, can be challenging for the planning system optimizer because of the complexity of the treatment and protected volumes, as well as striking heterogeneity corrections. Because it is often the goal of the planner to produce an isodose plan with uniform dose throughout the planning target volume (PTV), there is a need for improved planning optimization procedures for PTVs located in these anatomical regions. To illustrate such an improved procedure, we present a treatment planning case of a patient with a lung lesion located in the posterior right lung. The intensity-modulated radiation therapy (IMRT) plan generated using standard optimization procedures produced substantial dose nonuniformity across the tumor caused by the effect of lung tissue surrounding the tumor. We demonstrate a novel iterative method of dose correction performed on the initial IMRT plan to produce a more uniform dose distribution within the PTV. This optimization method corrected for the dose missing on the periphery of the PTV and reduced the maximum dose on the PTV to 106% from 120% on the representative IMRT plan.
Correcting biases in psychiatric diagnostic practice in Northwest Russia: Comparing the impact of a general educational program and a specific diagnostic training program
Directory of Open Access Journals (Sweden)
Rezvyy Grigory
Full Text Available Abstract Background A general education in psychiatry does not necessary lead to good diagnostic skills. Specific training programs in diagnostic coding are established to facilitate implementation of ICD-10 coding practices. However, studies comparing the impact of these two different educational approaches on diagnostic skills are lacking. The aim of the current study was to find out if a specific training program in diagnostic coding improves the diagnostic skills better than a general education program, and if a national bias in diagnostic patterns can be minimised by a specific training in diagnostic coding. Methods A pre post design study with two groups was carried in the county of Archangels, Russia. The control group (39 psychiatrists took the required course (general educational program, while the intervention group (45 psychiatrists were given a specific training in diagnostic coding. Their diagnostic skills before and after education were assessed using 12 written case-vignettes selected from the entire spectrum of psychiatric disorders. Results There was a significant improvement in diagnostic skills in both the intervention group and the control group. However, the intervention group improved significantly more than did the control group. The national bias was partly corrected in the intervention group but not to the same degree in the control group. When analyzing both groups together, among the background factors only the current working place impacted the outcome of the intervention. Conclusion Establishing an internationally accepted diagnosis seems to be a special skill that requires specific training and needs to be an explicit part of the professional educational activities of psychiatrists. It does not appear that that skill is honed without specific training. The issue of national diagnostic biases should be taken into account in comparative cross-cultural studies of almost any character. The mechanisms of such biases are
A Method for Estimating BeiDou Inter-frequency Satellite Clock Bias
Directory of Open Access Journals (Sweden)
LI Haojun
Full Text Available A new method for estimating the BeiDou inter-frequency satellite clock bias is proposed, considering the shortage of the current methods. The constant and variable parts of the inter-frequency satellite clock bias are considered in the new method. The data from 10 observation stations are processed to validate the new method. The characterizations of the BeiDou inter-frequency satellite clock bias are also analyzed using the computed results. The results of the BeiDou inter-frequency satellite clock bias indicate that it is stable in the short term. The estimated BeiDou inter-frequency satellite clock bias results are molded. The model results show that the 10 parameters of model for each satellite can express the BeiDou inter-frequency satellite clock bias well and the accuracy reaches cm level. When the model parameters of the first day are used to compute the BeiDou inter-frequency satellite clock bias of the second day, the accuracy also reaches cm level. Based on the stability and modeling, a strategy for the BeiDou satellite clock service is presented to provide the reference of our BeiDou.
A scanning tunneling microscope break junction method with continuous bias modulation.
Beall, Edward; Yin, Xing; Waldeck, David H; Wierzbinski, Emil
Single molecule conductance measurements on 1,8-octanedithiol were performed using the scanning tunneling microscope break junction method with an externally controlled modulation of the bias voltage. Application of an AC voltage is shown to improve the signal to noise ratio of low current (low conductance) measurements as compared to the DC bias method. The experimental results show that the current response of the molecule(s) trapped in the junction and the solvent media to the bias modulation can be qualitatively different. A model RC circuit which accommodates both the molecule and the solvent is proposed to analyze the data and extract a conductance for the molecule.
A Time-Walk Correction Method for PET Detectors Based on Leading Edge Discriminators.
Du, Junwei; Schmall, Jeffrey P; Judenhofer, Martin S; Di, Kun; Yang, Yongfeng; Cherry, Simon R
The leading edge timing pick-off technique is the simplest timing extraction method for PET detectors. Due to the inherent time-walk of the leading edge technique, corrections should be made to improve timing resolution, especially for time-of-flight PET. Time-walk correction can be done by utilizing the relationship between the threshold crossing time and the event energy on an event by event basis. In this paper, a time-walk correction method is proposed and evaluated using timing information from two identical detectors both using leading edge discriminators. This differs from other techniques that use an external dedicated reference detector, such as a fast PMT-based detector using constant fraction techniques to pick-off timing information. In our proposed method, one detector was used as reference detector to correct the time-walk of the other detector. Time-walk in the reference detector was minimized by using events within a small energy window (508.5 - 513.5 keV). To validate this method, a coincidence detector pair was assembled using two SensL MicroFB SiPMs and two 2.5 mm × 2.5 mm × 20 mm polished LYSO crystals. Coincidence timing resolutions using different time pick-off techniques were obtained at a bias voltage of 27.5 V and a fixed temperature of 20 °C. The coincidence timing resolution without time-walk correction were 389.0 ± 12.0 ps (425 -650 keV energy window) and 670.2 ± 16.2 ps (250-750 keV energy window). The timing resolution with time-walk correction improved to 367.3 ± 0.5 ps (425 - 650 keV) and 413.7 ± 0.9 ps (250 - 750 keV). For comparison, timing resolutions were 442.8 ± 12.8 ps (425 - 650 keV) and 476.0 ± 13.0 ps (250 - 750 keV) using constant fraction techniques, and 367.3 ± 0.4 ps (425 - 650 keV) and 413.4 ± 0.9 ps (250 - 750 keV) using a reference detector based on the constant fraction technique. These results show that the proposed leading edge based time-walk correction method works well. Timing resolution obtained
Method for exploiting bias in factor analysis using constrained alternating least squares algorithms
Keenan, Michael R.
Bias plays an important role in factor analysis and is often implicitly made use of, for example, to constrain solutions to factors that conform to physical reality. However, when components are collinear, a large range of solutions may exist that satisfy the basic constraints and fit the data equally well. In such cases, the introduction of mathematical bias through the application of constraints may select solutions that are less than optimal. The biased alternating least squares algorithm of the present invention can offset mathematical bias introduced by constraints in the standard alternating least squares analysis to achieve factor solutions that are most consistent with physical reality. In addition, these methods can be used to explicitly exploit bias to provide alternative views and provide additional insights into spectral data sets.
A method to correct sampling ghosts in historic near-infrared Fourier transform spectrometer (FTS) measurements
Dohe, S.; Sherlock, V.; Hase, F.; Gisi, M.; Robinson, J.; Sepúlveda, E.; Schneider, M.; Blumenstock, T.
The Total Carbon Column Observing Network (TCCON) has been established to provide ground-based remote sensing measurements of the column-averaged dry air mole fractions (DMF) of key greenhouse gases. To ensure network-wide consistency, biases between Fourier transform spectrometers at different sites have to be well controlled. Errors in interferogram sampling can introduce significant biases in retrievals. In this study we investigate a two-step scheme to correct these errors. In the first step the laser sampling error (LSE) is estimated by determining the sampling shift which minimises the magnitude of the signal intensity in selected, fully absorbed regions of the solar spectrum. The LSE is estimated for every day with measurements which meet certain selection criteria to derive the site-specific time series of the LSEs. In the second step, this sequence of LSEs is used to resample all the interferograms acquired at the site, and hence correct the sampling errors. Measurements acquired at the Izaña and Lauder TCCON sites are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions (e.g. realignment). Estimated LSEs are in good agreement with sampling errors inferred from the ratio of primary and ghost spectral signatures in optically bandpass-limited tungsten lamp spectra acquired at Lauder. The original time series of Xair and XCO2 (XY: column-averaged DMF of the target gas Y) at both sites show discrepancies of 0.2-0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling, discrepancies are reduced to 0.1% or less at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also have contributed to the residual difference. In the future the proposed method will be used to correct historical spectra at all TCCON sites.
A method to correct sampling ghosts in historic near-infrared Fourier transform spectrometer (FTS measurements
Directory of Open Access Journals (Sweden)
S. Dohe
Full Text Available The Total Carbon Column Observing Network (TCCON has been established to provide ground-based remote sensing measurements of the column-averaged dry air mole fractions (DMF of key greenhouse gases. To ensure network-wide consistency, biases between Fourier transform spectrometers at different sites have to be well controlled. Errors in interferogram sampling can introduce significant biases in retrievals. In this study we investigate a two-step scheme to correct these errors. In the first step the laser sampling error (LSE is estimated by determining the sampling shift which minimises the magnitude of the signal intensity in selected, fully absorbed regions of the solar spectrum. The LSE is estimated for every day with measurements which meet certain selection criteria to derive the site-specific time series of the LSEs. In the second step, this sequence of LSEs is used to resample all the interferograms acquired at the site, and hence correct the sampling errors. Measurements acquired at the Izaña and Lauder TCCON sites are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions (e.g. realignment. Estimated LSEs are in good agreement with sampling errors inferred from the ratio of primary and ghost spectral signatures in optically bandpass-limited tungsten lamp spectra acquired at Lauder. The original time series of Xair and XCO2 (XY: column-averaged DMF of the target gas Y at both sites show discrepancies of 0.2–0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling, discrepancies are reduced to 0.1% or less at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also have contributed to the residual difference. In the future the proposed method will be used to correct historical spectra at all TCCON sites.
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Directory of Open Access Journals (Sweden)
Isaac Mugume
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
"Racial bias in mock juror decision-making: A meta-analytic review of defendant treatment": Correction to Mitchell et al. (2005).
Reports an error in "Racial Bias in Mock Juror Decision-Making: A Meta-Analytic Review of Defendant Treatment" by Tara L. Mitchell, Ryann M. Haw, Jeffrey E. Pfeifer and Christian A. Meissner ( Law and Human Behavior , 2005[Dec], Vol 29[6], 621-637). In the article, all of the numbers in Appendix A were correct, but the signs were reversed for z' in a number of studies, which are listed. Also, in Appendix B, some values were incorrect, some signs were reversed, and some values were missing. The corrected appendix is included. (The following abstract of the original article appeared in record 2006-00971-001.) Common wisdom seems to suggest that racial bias, defined as disparate treatment of minority defendants, exists in jury decision-making, with Black defendants being treated more harshly by jurors than White defendants. The empirical research, however, is inconsistent--some studies show racial bias while others do not. Two previous meta-analyses have found conflicting results regarding the existence of racial bias in juror decision-making (Mazzella & Feingold, 1994, Journal of Applied Social Psychology, 24, 1315-1344; Sweeney & Haney, 1992, Behavioral Sciences and the Law, 10, 179-195). This research takes a meta-analytic approach to further investigate the inconsistencies within the empirical literature on racial bias in juror decision-making by defining racial bias as disparate treatment of racial out-groups (rather than focusing upon the minority group alone). Our results suggest that a small, yet significant, effect of racial bias in decision-making is present across studies, but that the effect becomes more pronounced when certain moderators are considered. The state of the research will be discussed in light of these findings. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Auto correct method of AD converters precision based on ethernet
Directory of Open Access Journals (Sweden)
NI Jifeng
Full Text Available Ideal AD conversion should be a straight zero-crossing line in the Cartesian coordinate axis system. While in practical engineering, the signal processing circuit, chip performance and other factors have an impact on the accuracy of conversion. Therefore a linear fitting method is adopted to improve the conversion accuracy. An automatic modification of AD conversion based on Ethernet is presented by using software and hardware. Just by tapping the mouse, all the AD converter channel linearity correction can be automatically completed, and the error, SNR and ENOB (effective number of bits are calculated. Then the coefficients of linear modification are loaded into the onboard AD converter card's EEPROM. Compared with traditional methods, this method is more convenient, accurate and efficient,and has a broad application prospects.
A method for the quantification of biased signalling at constitutively active receptors.
Hall, David A; Giraldo, Jesús
Biased agonism, the ability of an agonist to differentially activate one of several signal transduction pathways when acting at a given receptor, is an increasingly recognized phenomenon at many receptors. The Black and Leff operational model lacks a way to describe constitutive receptor activity and hence inverse agonism. Thus, it is impossible to analyse the biased signalling of inverse agonists using this model. In this theoretical work, we develop and illustrate methods for the analysis of biased inverse agonism. Methods were derived for quantifying biased signalling in systems that demonstrate constitutive activity using the modified operational model proposed by Slack and Hall. The methods were illustrated using Monte Carlo simulations. The Monte Carlo simulations demonstrated that, with an appropriate experimental design, the model parameters are 'identifiable'. The method is consistent with methods based on the measurement of intrinsic relative activity (RA i ) (ΔΔlogR or ΔΔlog(τ/K a )) proposed by Ehlert and Kenakin and their co-workers but has some advantages. In particular, it allows the quantification of ligand bias independently of 'system bias' removing the requirement to normalize to a standard ligand. In systems with constitutive activity, the Slack and Hall model provides methods for quantifying the absolute bias of agonists and inverse agonists. This provides an alternative to methods based on RA i and is complementary to the ΔΔlog(τ/K a ) method of Kenakin et al. in systems where use of that method is inappropriate due to the presence of constitutive activity. © 2018 The British Pharmacological Society.
Tile Calorimeter modules stored at CERN. The larger modules belong to the Barrel, whereas the smaller ones are for the two Extended Barrels. (The article was about the completion of the 64 modules for one of the latter.) The photo on the first page of the Bulletin n°26/2002, from 24 July 2002, illustrating the article «The ATLAS Tile Calorimeter gets into shape» was published with a wrong caption. We would like to apologise for this mistake and so publish it again with the correct caption.
Filtering of SPECT reconstructions made using Bellini's attenuation correction method
International Nuclear Information System (INIS)
Glick, S.J.; Penney, B.C.; King, M.A.
This paper evaluates a three-dimensional (3D) Wiener filter which is used to restore SPECT reconstructions which were made using Bellini's method of attenuation correction. Its performance is compared to that of several pre-reconstruction filers: the one-dimensional (1D) Butterworth, the two-dimensional (2D) Butterworth, and a 2D Wiener filer. A simulation study is used to compare the four filtering methods. An approximation to a clinical liver spleen study was used as the source distribution and algorithm which accounts for the depth and distance dependent blurring in SPECT was used to compute noise free projections. To study the effect of filtering method on tumor detection accuracy, a 2 cm diameter, cool spherical tumor (40% contrast) was placed at a known, but random, location with the liver. Projection sets for ten tumor locations were computed and five noise realizations of each set were obtained by introducing Poisson noise. The simulated projections were either: filtered with the 1D or 2D Butterworth or the 2D Wiener and then reconstructed using Bellini's intrinsic attenuation correction, or reconstructed first, then filtered with the 3D Wiener. The criteria used for comparison were: normalized mean square error (NMSE), cold spot contrast, and accuracy of tumor detection with an automated numerical method. Results indicate that restorations obtained with 3D Wiener filtering yielded significantly higher lesion contrast and lower NMSE values compared to the other methods of processing. The Wiener restoration filters and the 2D Butterworth all provided similar measures of detectability, which were noticeably higher than that obtained with 1D Butterworth smoothing
Biasing transition rate method based on direct MC simulation for probabilistic safety assessment
Institute of Scientific and Technical Information of China (English)
Xiao-Lei Pan; Jia-Qun Wang; Run Yuan; Fang Wang; Han-Qing Lin; Li-Qin Hu; Jin Wang
Direct Monte Carlo (MC) simulation is a powerful probabilistic safety assessment method for accounting dynamics of the system.But it is not efficient at simulating rare events.A biasing transition rate method based on direct MC simulation is proposed to solve the problem in this paper.This method biases transition rates of the components by adding virtual components to them in series to increase the occurrence probability of the rare event,hence the decrease in the variance of MC estimator.Several cases are used to benchmark this method.The results show that the method is effective at modeling system failure and is more efficient at collecting evidence of rare events than the direct MC simulation.The performance is greatly improved by the biasing transition rate method.
Exploring Gender Biases in a General Methods Class.
Quinn, Robert J.; Obenchain, Kathryn M.
Describes how students in a general secondary methods course responded to a gender-neutral exam question by consistently assuming that the student in the hypothetical scenario was male. Describes the follow-up class discussion, noting students' assumptions, defensive responses, subconscious decision making, and awareness/nonawareness of their own…
Development and Assessment of a Bundle Correction Method for CHF
International Nuclear Information System (INIS)
Hwang, Dae Hyun; Chang, Soon Heung
A bundle correction method, based on the conservation laws of mass, energy, and momentum in an open subchannel, is proposed for the prediction of the critical heat flux (CHF) in rod bundles from round tube CHF correlations without detailed subchannel analysis. It takes into account the effects of the enthalpy and mass velocity distributions at subchannel level using the first dericatives of CHF with respect to the independent parameters. Three different CHF correlations for tubes (Groeneveld's CHF table, Katto correlation, and Biasi correlation) have been examined with uniformly heated bundle CHF data collected from various sources. A limited number of GHE data from a non-uniformly heated rod bundle are also evaluated with the aid of Tong's F-factor. The proposed method shows satisfactory CHF predictions for rod bundles both uniform and non-uniform power distributions. (Author)
Empirical method for matrix effects correction in liquid samples
International Nuclear Information System (INIS)
Vigoda de Leyt, Dora; Vazquez, Cristina
A simple method for the determination of Cr, Ni and Mo in stainless steels is presented. In order to minimize the matrix effects, the conditions of liquid system to dissolve stainless steels chips has been developed. Pure element solutions were used as standards. Preparation of synthetic solutions with all the elements of steel and also mathematic corrections are avoided. It results in a simple chemical operation which simplifies the method of analysis. The variance analysis of the results obtained with steel samples show that the three elements may be determined from the comparison with the analytical curves obtained with the pure elements if the same parameters in the calibration curves are used. The accuracy and the precision were checked against other techniques using the British Chemical Standards of the Bureau of Anlysed Samples Ltd. (England). (M.E.L.) [es
Directory of Open Access Journals (Sweden)
Full Text Available Regarding Gorelik, G., & Shackelford, T.K. (2011. Human sexual conflict from molecules to culture. Evolutionary Psychology, 9, 564–587: The authors wish to correct an omission in citation to the existing literature. In the final paragraph on p. 570, we neglected to cite Burch and Gallup (2006 [Burch, R. L., & Gallup, G. G., Jr. (2006. The psychobiology of human semen. In S. M. Platek & T. K. Shackelford (Eds., Female infidelity and paternal uncertainty (pp. 141–172. New York: Cambridge University Press.]. Burch and Gallup (2006 reviewed the relevant literature on FSH and LH discussed in this paragraph, and should have been cited accordingly. In addition, Burch and Gallup (2006 should have been cited as the originators of the hypothesis regarding the role of FSH and LH in the semen of rapists. The authors apologize for this oversight.
The photo on the second page of the Bulletin n°48/2002, from 25 November 2002, illustrating the article «Spanish Visit to CERN» was published with a wrong caption. We would like to apologise for this mistake and so publish it again with the correct caption. The Spanish delegation, accompanied by Spanish scientists at CERN, also visited the LHC superconducting magnet test hall (photo). From left to right: Felix Rodriguez Mateos of CERN LHC Division, Josep Piqué i Camps, Spanish Minister of Science and Technology, César Dopazo, Director-General of CIEMAT (Spanish Research Centre for Energy, Environment and Technology), Juan Antonio Rubio, ETT Division Leader at CERN, Manuel Aguilar-Benitez, Spanish Delegate to Council, Manuel Delfino, IT Division Leader at CERN, and Gonzalo León, Secretary-General of Scientific Policy to the Minister.
[Case-non case studies: Principles, methods, bias and interpretation].
Faillie, Jean-Luc
Case-non case studies belongs to the methods assessing drug safety by analyzing the disproportionality of notifications of adverse drug reactions in pharmacovigilance databases. Used for the first time in the 1980s, the last few decades have seen a significant increase in the use of this design. The principle of the case-non case study is to compare drug exposure in cases of a studied adverse reaction with that of cases of other reported adverse reactions and called "non cases". Results are presented in the form of a reporting odds ratio (ROR), the interpretation of which makes it possible to identify drug safety signals. This article describes the principle of the case-non case study, the method of calculating the ROR and its confidence interval, the different modalities of analysis and how to interpret its results with regard to the advantages and limitations of this design. Copyright © 2017 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.
Directory of Open Access Journals (Sweden)
Full Text Available Regarding Tagler, M. J., and Jeffers, H. M. (2013. Sex differences in attitudes toward partner infidelity. Evolutionary Psychology, 11, 821–832: The authors wish to correct values in the originally published manuscript. Specifically, incorrect 95% confidence intervals around the Cohen's d values were reported on page 826 of the manuscript where we reported the within-sex simple effects for the significant Participant Sex × Infidelity Type interaction (first paragraph, and for attitudes toward partner infidelity (second paragraph. Corrected values are presented in bold below. The authors would like to thank Dr. Bernard Beins at Ithaca College for bringing these errors to our attention. Men rated sexual infidelity significantly more distressing (M = 4.69, SD = 0.74 than they rated emotional infidelity (M = 4.32, SD = 0.92, F(1, 322 = 23.96, p < .001, d = 0.44, 95% CI [0.23, 0.65], but there was little difference between women's ratings of sexual (M = 4.80, SD = 0.48 and emotional infidelity (M = 4.76, SD = 0.57, F(1, 322 = 0.48, p = .29, d = 0.08, 95% CI [−0.10, 0.26]. As expected, men rated sexual infidelity (M = 1.44, SD = 0.70 more negatively than they rated emotional infidelity (M = 2.66, SD = 1.37, F(1, 322 = 120.00, p < .001, d = 1.12, 95% CI [0.85, 1.39]. Although women also rated sexual infidelity (M = 1.40, SD = 0.62 more negatively than they rated emotional infidelity (M = 2.09, SD = 1.10, this difference was not as large and thus in the evolutionary theory supportive direction, F(1, 322 = 72.03, p < .001, d = 0.77, 95% CI [0.60, 0.94].
Gynecomastia: the horizontal ellipse method for its correction.
Gheita, Alaa
Gynecomastia is an extremely disturbing deformity affecting males, especially when it occurs in young subjects. Such subjects generally have no hormonal anomalies and thus either liposuction or surgical intervention, depending on the type and consistency of the breast, is required for treatment. If there is slight hypertrophy alone with no ptosis, then subcutaneous mastectomy is usually sufficient. However, when hypertrophy and/or ptosis are present, then corrective surgery on the skin and breast is mandatory to obtain a good cosmetic result. Most of the procedures suggested for reduction of the male breast are usually derived from reduction mammaplasty methods used for females. They have some disadvantages, mainly the multiple scars, which remain apparent in males, unusual shape, and the lack of symmetry with regard to the size of both breasts and/or the nipple position. The author presents a new, simple method that has proven superior to any previous method described so far. It consists of a horizontal excision ellipse of the breast's redundant skin and deep excess tissue and a superior pedicle flap carrying the areola-nipple complex to its new site on the chest wall. The method described yields excellent shape, symmetry, and minimal scars. A new method for treating gynecomastis is described in detail, its early and late operative results are shown, and its advantages are discussed.
Climate projections and extremes in dynamically downscaled CMIP5 model outputs over the Bengal delta: a quartile based bias-correction approach with new gridded data
Hasan, M. Alfi; Islam, A. K. M. Saiful; Akanda, Ali Shafqat
In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071-2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected
Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.
Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard
In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.
A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes
Wang, Chaolong; Schroeder, Kari B.; Rosenberg, Noah A.
Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets
A Conceptually Simple Modeling Approach for Jason-1 Sea State Bias Correction Based on 3 Parameters Exclusively Derived from Altimetric Information
Directory of Open Access Journals (Sweden)
Nelson Pires
Full Text Available A conceptually simple formulation is proposed for a new empirical sea state bias (SSB model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH and wind speed (U10, a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions.
Biogeosystem Technique as a method to correct the climate
Kalinitchenko, Valery; Batukaev, Abdulmalik; Batukaev, Magomed; Minkina, Tatiana
can be produced; The less energy is consumed for climate correction, the better. The proposed algorithm was never discussed before because most of its ingredients were unenforceable. Now the possibility to execute the algorithm exists in the framework of our new scientific-technical branch - Biogeosystem Technique (BGT*). The BGT* is a transcendental (non-imitating natural processes) approach to soil processing, regulation of energy, matter, water fluxes and biological productivity of biosphere: intra-soil machining to provide the new highly productive dispersed system of soil; intra-soil pulse continuous-discrete plants watering to reduce the transpiration rate and water consumption of plants for 5-20 times; intra-soil environmentally safe return of matter during intra-soil milling processing and (or) intra-soil pulse continuous-discrete plants watering with nutrition. Are possible: waste management; reducing flow of nutrients to water systems; carbon and other organic and mineral substances transformation into the soil to plant nutrition elements; less degradation of biological matter to greenhouse gases; increasing biological sequestration of carbon dioxide in terrestrial system's photosynthesis; oxidizing methane and hydrogen sulfide by fresh photosynthesis ionized biologically active oxygen; expansion of the active terrestrial site of biosphere. The high biological product output of biosphere will be gained. BGT* robotic systems are of low cost, energy and material consumption. By BGT* methods the uncertainties of climate and biosphere will be reduced. Key words: Biogeosystem Technique, method to correct, climate
Diagnostics and correction of disregulation states by physical methods
Gorsha, O. V.; Gorsha, V. I.
Nicolaus Copernicus University, Toruń, Poland Ukrainian Research Institute for Medicine of Transport, Odesa, Ukraine Gorsha O. V., Gorsha V. I. Diagnostics and correction of disregulation states by physical methods Горша О. В., Горша В. И. Диагностика и коррекция физическими методами дизрегуляторных состояний Toruń, Odesa 2017 Nicolaus Copernicus University, To...
A 2 × 2 taxonomy of multilevel latent contextual models: accuracy-bias trade-offs in full and partial error correction models.
Lüdtke, Oliver; Marsh, Herbert W; Robitzsch, Alexander; Trautwein, Ulrich
In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data when estimating contextual effects are distinguished: unreliability that is due to measurement error and unreliability that is due to sampling error. The fact that studies may or may not correct for these 2 types of error can be translated into a 2 × 2 taxonomy of multilevel latent contextual models comprising 4 approaches: an uncorrected approach, partial correction approaches correcting for either measurement or sampling error (but not both), and a full correction approach that adjusts for both sources of error. It is shown mathematically and with simulated data that the uncorrected and partial correction approaches can result in substantially biased estimates of contextual effects, depending on the number of L1 individuals per group, the number of groups, the intraclass correlation, the number of indicators, and the size of the factor loadings. However, the simulation study also shows that partial correction approaches can outperform full correction approaches when the data provide only limited information in terms of the L2 construct (i.e., small number of groups, low intraclass correlation). A real-data application from educational psychology is used to illustrate the different approaches.
Experimental aspects of buoyancy correction in measuring reliable high-pressure excess adsorption isotherms using the gravimetric method
Nguyen, Huong Giang T.; Horn, Jarod C.; Thommes, Matthias; van Zee, Roger D.; Espinal, Laura
Addressing reproducibility issues in adsorption measurements is critical to accelerating the path to discovery of new industrial adsorbents and to understanding adsorption processes. A National Institute of Standards and Technology Reference Material, RM 8852 (ammonium ZSM-5 zeolite), and two gravimetric instruments with asymmetric two-beam balances were used to measure high-pressure adsorption isotherms. This work demonstrates how common approaches to buoyancy correction, a key factor in obtaining the mass change due to surface excess gas uptake from the apparent mass change, can impact the adsorption isotherm data. Three different approaches to buoyancy correction were investigated and applied to the subcritical CO2 and supercritical N2 adsorption isotherms at 293 K. It was observed that measuring a collective volume for all balance components for the buoyancy correction (helium method) introduces an inherent bias in temperature partition when there is a temperature gradient (i.e. analysis temperature is not equal to instrument air bath temperature). We demonstrate that a blank subtraction is effective in mitigating the biases associated with temperature partitioning, instrument calibration, and the determined volumes of the balance components. In general, the manual and subtraction methods allow for better treatment of the temperature gradient during buoyancy correction. From the study, best practices specific to asymmetric two-beam balances and more general recommendations for measuring isotherms far from critical temperatures using gravimetric instruments are offered.
Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.
Johnson, Eric O; Hancock, Dana B; Levy, Joshua L; Gaddis, Nathan C; Saccone, Nancy L; Bierut, Laura J; Page, Grier P
A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.
A New Navigation Satellite Clock Bias Prediction Method Based on Modified Clock-bias Quadratic Polynomial Model
Wang, Y. P.; Lu, Z. P.; Sun, D. S.; Wang, N.
In order to better express the characteristics of satellite clock bias (SCB) and improve SCB prediction precision, this paper proposed a new SCB prediction model which can take physical characteristics of space-borne atomic clock, the cyclic variation, and random part of SCB into consideration. First, the new model employs a quadratic polynomial model with periodic items to fit and extract the trend term and cyclic term of SCB; then based on the characteristics of fitting residuals, a time series ARIMA ~(Auto-Regressive Integrated Moving Average) model is used to model the residuals; eventually, the results from the two models are combined to obtain final SCB prediction values. At last, this paper uses precise SCB data from IGS (International GNSS Service) to conduct prediction tests, and the results show that the proposed model is effective and has better prediction performance compared with the quadratic polynomial model, grey model, and ARIMA model. In addition, the new method can also overcome the insufficiency of the ARIMA model in model recognition and order determination.
Novel, cyclic heat dissipation method for the correction of natural temperature gradients in sap flow measurements. Part 2. Laboratory validation.
Reyes-Acosta, J Leonardo; Vandegehuchte, Maurits W; Steppe, Kathy; Lubczynski, Maciek W
Sap flow measurements conducted with thermal dissipation probes (TDPs) are vulnerable to natural temperature gradient (NTG) bias. Few studies, however, attempted to explain the dynamics underlying the NTG formation and its influence on the sensors' signal. This study focused on understanding how the TDP signals are affected by negative and positive temperature influences from NTG and tested the novel cyclic heat dissipation (CHD) method to filter out the NTG bias. A series of three experiments were performed in which gravity-driven water flow was enforced on freshly cut stem segments of Fagus sylvatica L., while an artificial temperature gradient (ATG) was induced. The first experiment sought to confirm the incidence of the ATG on sensors. The second experiment established the mis-estimations caused by the biasing effect of the ATG on standard TDP measurements. The third experiment tested the accuracy of the CHD method to account for the ATG biasing effect, as compared with other cyclic correction methods. During experiments, sap flow measured by TDP was assessed against gravimetric measurements. The results show that negative and positive ATGs were comparable in pattern but substantially larger than field NTGs. Second, the ATG bias caused an overestimation of the standard TDP sap flux density of ∼17 cm(3) cm(-2) h(-1) by 76%, and the sap flux density of ∼2 cm(3) cm(-2) h(-1) by over 800%. Finally, the proposed CHD method successfully reduced the max. ATG bias to 25% at ∼11 cm(3) cm(-2) h(-1) and to 40% at ∼1 cm(3) cm(-2) h(-1). We concluded that: (i) the TDP method is susceptible to NTG especially at low flows; (ii) the CHD method successfully corrected the TDP signal and resulted in generally more accurate sap flux density estimates (mean absolute percentage error ranging between 11 and 21%) than standard constant power TDP method and other cyclic power methods; and (iii) the ATG enforcing system is a suitable way of re-creating NTG for future tests.
The response of future projections of the North American monsoon when combining dynamical downscaling and bias correction of CCSM4 output
Meyer, Jonathan D. D.; Jin, Jiming
A 20-km regional climate model (RCM) dynamically downscaled the Community Climate System Model version 4 (CCSM4) to compare 32-year historical and future "end-of-the-century" climatologies of the North American Monsoon (NAM). CCSM4 and other phase 5 Coupled Model Intercomparison Project models have indicated a delayed NAM and overall general drying trend. Here, we test the suggested mechanism for this drier NAM where increasing atmospheric static stability and reduced early-season evapotranspiration under global warming will limit early-season convection and compress the mature-season of the NAM. Through our higher resolution RCM, we found the role of accelerated evaporation under a warmer climate is likely understated in coarse resolution models such as CCSM4. Improving the representation of mesoscale interactions associated with the Gulf of California and surrounding topography produced additional surface evaporation, which overwhelmed the convection-suppressing effects of a warmer troposphere. Furthermore, the improved land-sea temperature gradient helped drive stronger southerly winds and greater moisture transport. Finally, we addressed limitations from inherent CCSM4 biases through a form of mean bias correction, which resulted in a more accurate seasonality of the atmospheric thermodynamic profile. After bias correction, greater surface evaporation from average peak GoC SSTs of 32 °C compared to 29 °C from the original CCSM4 led to roughly 50 % larger changes to low-level moist static energy compared to that produced by the downscaled original CCSM4. The increasing destabilization of the NAM environment produced onset dates that were one to 2 weeks earlier in the core of the NAM and northern extent, respectively. Furthermore, a significantly more vigorous NAM signal was produced after bias correction, with >50 mm month-1 increases to the June-September precipitation found along east and west coasts of Mexico and into parts of Texas. A shift towards more
Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study
Directory of Open Access Journals (Sweden)
In Sung Cho
Full Text Available Abstract Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE. In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.
Global daily precipitation fields from bias-corrected rain gauge and satellite observations. Pt. 1. Design and development
Energy Technology Data Exchange (ETDEWEB)
Kottek, M.; Rubel, F. [Univ. of Veterinary Medicine, Vienna (Austria). Biometeorology Group
Global daily precipitation analyses are mainly based on satellite estimates, often calibrated with monthly ground analyses or merged with model predictions. We argue here that an essential improvement of their accuracy is only possible by incorporation of daily ground measurements. In this work we apply geostatistical methods to compile a global precipitation product based on daily rain gauge measurements. The raw ground measurements, disseminated via Global Telecommunication System (GTS), are corrected for their systematic measurement errors and interpolated onto a global 1 degree grid. For interpolation ordinary block kriging is applied, with precalculated spatial auto-correlation functions (ACFs). This technique allows to incorporate additional climate information. First, monthly ACFs are calculated from the daily data; second, they are regionalised according to the five main climatic zones of the Koeppen-Geiger climate classification. The interpolation error, a by-product of kriging, is used to flag grid points as missing if the error is above a predefined threshold. But for many applications missing values constitute a problem. Due to a combination of the ground analyses with the daily multi-satellite product of the Global Precipitation Climatology Project (GPCP-1DD) not only these missing values are replaced but also the spatial structure of the satellite estimates is considered. As merging method bivariate ordinary co-kriging is applied. The ACFs necessary for the gauge and the satellite fields as well as the corresponding spatial cross-correlation functions (CCFs) are again precalculated for each of the five main climatic zones and for each individual month. As a result two new global daily data sets for the period 1996 up to today will be available on the Internet (www.gmes-geoland.info): A precipitation product over land, analysed from ground measurements; and a global precipitation product merged from this and the GPCP-1DD multi-satellite product. (orig.)
Method and system of doppler correction for mobile communications systems
Georghiades, Costas N. (Inventor); Spasojevic, Predrag (Inventor)
Doppler correction system and method comprising receiving a Doppler effected signal comprising a preamble signal (32). A delayed preamble signal (48) may be generated based on the preamble signal (32). The preamble signal (32) may be multiplied by the delayed preamble signal (48) to generate an in-phase preamble signal (60). The in-phase preamble signal (60) may be filtered to generate a substantially constant in-phase preamble signal (62). A plurality of samples of the substantially constant in-phase preamble signal (62) may be accumulated. A phase-shifted signal (76) may also be generated based on the preamble signal (32). The phase-shifted signal (76) may be multiplied by the delayed preamble signal (48) to generate an out-of-phase preamble signal (80). The out-of-phase preamble signal (80) may be filtered to generate a substantially constant out-of-phase preamble signal (82). A plurality of samples of the substantially constant out-of-phase signal (82) may be accumulated. A sum of the in-phase preamble samples and a sum of the out-of-phase preamble samples may be normalized relative to each other to generate an in-phase Doppler estimator (92) and an out-of-phase Doppler estimator (94).
Relative equilibrium plot improves graphical analysis and allows bias correction of SUVR in quantitative [11C]PiB PET studies
Zhou, Yun; Sojkova, Jitka; Resnick, Susan M.; Wong, Dean F.
Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVR) in ligand-receptor dynamic PET studies. The objective of this study is to use a recently developed relative equilibrium-based graphical plot (RE plot) method to improve and simplify the two commonly used methods for quantification of [11C]PiB PET.
An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method
Directory of Open Access Journals (Sweden)
Jingyang Fu
Full Text Available Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB, which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP. Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL time to first fix (TTFF in PPP ambiguity resolution (AR as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have
RNA preservation agents and nucleic acid extraction method bias perceived bacterial community composition.
Directory of Open Access Journals (Sweden)
Ann McCarthy
Full Text Available Bias is a pervasive problem when characterizing microbial communities. An important source is the difference in lysis efficiencies of different populations, which vary depending on the extraction protocol used. To avoid such biases impacting comparisons between gene and transcript abundances in the environment, the use of one protocol that simultaneously extracts both types of nucleic acids from microbial community samples has gained popularity. However, knowledge regarding tradeoffs to combined nucleic acid extraction protocols is limited, particularly regarding yield and biases in the observed community composition. Here, we evaluated a commercially available protocol for simultaneous extraction of DNA and RNA, which we adapted for freshwater microbial community samples that were collected on filters. DNA and RNA yields were comparable to other commonly used, but independent DNA and RNA extraction protocols. RNA protection agents benefited RNA quality, but decreased DNA yields significantly. Choice of extraction protocol influenced the perceived bacterial community composition, with strong method-dependent biases observed for specific phyla such as the Verrucomicrobia. The combined DNA/RNA extraction protocol detected significantly higher levels of Verrucomicrobia than the other protocols, and those higher numbers were confirmed by microscopic analysis. Use of RNA protection agents as well as independent sequencing runs caused a significant shift in community composition as well, albeit smaller than the shift caused by using different extraction protocols. Despite methodological biases, sample origin was the strongest determinant of community composition. However, when the abundance of specific phylogenetic groups is of interest, researchers need to be aware of the biases their methods introduce. This is particularly relevant if different methods are used for DNA and RNA extraction, in addition to using RNA protection agents only for RNA
A forecasting method to reduce estimation bias in self-reported cell phone data.
Redmayne, Mary; Smith, Euan; Abramson, Michael J
There is ongoing concern that extended exposure to cell phone electromagnetic radiation could be related to an increased risk of negative health effects. Epidemiological studies seek to assess this risk, usually relying on participants' recalled use, but recall is notoriously poor. Our objectives were primarily to produce a forecast method, for use by such studies, to reduce estimation bias in the recalled extent of cell phone use. The method we developed, using Bayes' rule, is modelled with data we collected in a cross-sectional cluster survey exploring cell phone user-habits among New Zealand adolescents. Participants recalled their recent extent of SMS-texting and retrieved from their provider the current month's actual use-to-date. Actual use was taken as the gold standard in the analyses. Estimation bias arose from a large random error, as observed in all cell phone validation studies. We demonstrate that this seriously exaggerates upper-end forecasts of use when used in regression models. This means that calculations using a regression model will lead to underestimation of heavy-users' relative risk. Our Bayesian method substantially reduces estimation bias. In cases where other studies' data conforms to our method's requirements, application should reduce estimation bias, leading to a more accurate relative risk calculation for mid-to-heavy users.
Are Teacher Course Evaluations Biased against Faculty That Teach Quantitative Methods Courses?
Royal, Kenneth D.; Stockdale, Myrah R.
The present study investigated graduate students' responses to teacher/course evaluations (TCE) to determine if students' responses were inherently biased against faculty who teach quantitative methods courses. Item response theory (IRT) and Differential Item Functioning (DIF) techniques were utilized for data analysis. Results indicate students…
Gamma camera correction system and method for using the same
International Nuclear Information System (INIS)
Inbar, D.; Gafni, G.; Grimberg, E.; Bialick, K.; Koren, J.
A gamma camera is described which consists of: (a) a detector head that includes photodetectors for producing output signals in response to radiation stimuli which are emitted by a radiation field and which interact with the detector head and produce an event; (b) signal processing circuitry responsive to the output signals of the photodetectors for producing a sum signal that is a measure of the total energy of the event; (c) an energy discriminator having a relatively wide window for comparison with the sum signal; (d) the signal processing circuitry including coordinate computation circuitry for operating on the output signals, and calculating an X,Y coordinate of an event when the sum signal lies within the window of the energy discriminator; (e) an energy correction table containing spatially dependent energy windows for producing a validation signal if the total energy of an event lies within the window associated with the X,Y coordinates of the event; (f) the signal processing circuitry including a dislocation correction table containing spatially dependent correction factors for converting the X,Y coordinates of an event to relocated coordinates in accordance with correction factors determined by the X,Y coordinates; (g) a digital memory for storing a map of the radiation field; and (h) means for recording an event at its relocated coordinates in the memory if the energy correction table produces a validation signal
Effect of methods of myopia correction on visual acuity, contrast sensitivity, and depth of focus
Nio, YK; Jansonius, NM; Wijdh, RHJ; Beekhuis, WH; Worst, JGF; Noorby, S; Kooijman, AC
Purpose. To psychophysically measure spherical and irregular aberrations in patients with various types of myopia correction. Setting: Laboratory of Experimental Ophthalmology, University of Groningen, Groningen, The Netherlands. Methods: Three groups of patients with low myopia correction
Peculiarities of application the method of autogenic training in the correction of eating behavior
Shebanova, Vitaliya
The article presented peculiarities of applying the method of autogenic training in the correction of eating disorders. Described stages of correction work with desadaptive eating behavior. Author makes accent on the rules self-assembly formula intentions.
Methods and apparatus for environmental correction of thermal neutron logs
International Nuclear Information System (INIS)
Preeg, W.E.; Scott, H.D.
An on-line environmentally-corrected measurement of the thermal neutron decay time (tau) of an earth formation traversed by a borehole is provided in a two-detector, pulsed neutron logging tool, by measuring tau at each detector and combining the two tau measurements in accordance with a previously established empirical relationship of the general form: tau = tausub(F) +A(tausub(F) + tausub(N)B) + C, where tausub(F) and tausub(N) are the tau measurements at the far-spaced and near-spaced detectors, respectively, A is a correction coefficient for borehole capture cross section effects, B is a correction coefficient for neutron diffusion effects, and C is a constant related to parameters of the logging tool. Preferred numerical values of A, B and C are disclosed, and a relationship for more accurately approximating the A term to specific borehole conditions. (author)
Correcting for Systematic Bias in Sample Estimates of Population Variances: Why Do We Divide by n-1?
Mittag, Kathleen Cage
An important topic presented in introductory statistics courses is the estimation of population parameters using samples. Students learn that when estimating population variances using sample data, we always get an underestimate of the population variance if we divide by n rather than n-1. One implication of this correction is that the degree of…
Evaluation of Bias Correction Methods for "Worst-case" Selective Non-participation in NAEP
McLaughlin, Don; Gallagher, Larry; Stancavage, Fran
With the advent of No Child Left Behind (NCLB), the context for NAEP participation is changing. Whereas in the past participation in NAEP has always been voluntary, participation is now mandatory for some grade and subjects among schools receiving Title I funds. While this will certainly raise school-level participation rates in the mandated…
Practical method of breast attenuation correction for cardiac SPECT
International Nuclear Information System (INIS)
Oliveira, Anderson de; Nogueira, Tindyua; Gutterres, Ricardo Fraga; Megueriam, Berdj Aram; Santos, Goncalo Rodrigues dos
The breast attenuation effects on SPECT (Single Photon Emission Tomography) myocardium perfusion procedures have been lately scope of continuous inquiry. The requested attenuation correction factors are usually achieved by transmission analysis, making up the exposure of a standard external source to the SPECT, as a routine step. However, its high cost makes this methodology not fully available to the most of nuclear medicines services in Brazil and abroad. To overcome the problem, a new trend is presented in this work, implementing computational models to balance the breast attenuation effects on the left ventricle anterior wall, during myocardium perfusion scintigraphy procedures with SPECT. A neural network was put on in order to provide the attenuation correction indexes, based upon the following patients individual biotypes features: mass, age, height, chest and breast thicknesses, heart size, as well as the imparted activity intake levels. (author)
Practical method of breast attenuation correction for cardiac SPECT
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Anderson de; Nogueira, Tindyua; Gutterres, Ricardo Fraga [Comissao Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ (Brazil). Coordenacao Geral de Instalacoes Medicas e Industriais (CGMI)]. E-mails: email@example.com; firstname.lastname@example.com; firstname.lastname@example.com; Megueriam, Berdj Aram [Instituto Nacional do Cancer (INCA), Rio de Janeiro, RJ (Brazil)]. E-mail: firstname.lastname@example.com; Santos, Goncalo Rodrigues dos [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)]. E-mail: firstname.lastname@example.com
The breast attenuation effects on SPECT (Single Photon Emission Tomography) myocardium perfusion procedures have been lately scope of continuous inquiry. The requested attenuation correction factors are usually achieved by transmission analysis, making up the exposure of a standard external source to the SPECT, as a routine step. However, its high cost makes this methodology not fully available to the most of nuclear medicines services in Brazil and abroad. To overcome the problem, a new trend is presented in this work, implementing computational models to balance the breast attenuation effects on the left ventricle anterior wall, during myocardium perfusion scintigraphy procedures with SPECT. A neural network was put on in order to provide the attenuation correction indexes, based upon the following patients individual biotypes features: mass, age, height, chest and breast thicknesses, heart size, as well as the imparted activity intake levels. (author)
Generation of future potential scenarios in an Alpine Catchment by applying bias-correction techniques, delta-change approaches and stochastic Weather Generators at different spatial scale. Analysis of their influence on basic and drought statistics.
Collados-Lara, Antonio-Juan; Pulido-Velazquez, David; Pardo-Iguzquiza, Eulogio
Assessing impacts of potential future climate change scenarios in precipitation and temperature is essential to design adaptive strategies in water resources systems. The objective of this work is to analyze the possibilities of different statistical downscaling methods to generate future potential scenarios in an Alpine Catchment from historical data and the available climate models simulations performed in the frame of the CORDEX EU project. The initial information employed to define these downscaling approaches are the historical climatic data (taken from the Spain02 project for the period 1971-2000 with a spatial resolution of 12.5 Km) and the future series provided by climatic models in the horizon period 2071-2100 . We have used information coming from nine climate model simulations (obtained from five different Regional climate models (RCM) nested to four different Global Climate Models (GCM)) from the European CORDEX project. In our application we have focused on the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC). For each RCM we have generated future climate series for the period 2071-2100 by applying two different approaches, bias correction and delta change, and five different transformation techniques (first moment correction, first and second moment correction, regression functions, quantile mapping using distribution derived transformation and quantile mapping using empirical quantiles) for both of them. Ensembles of the obtained series were proposed to obtain more representative potential future climate scenarios to be employed to study potential impacts. In this work we propose a non-equifeaseble combination of the future series giving more weight to those coming from models (delta change approaches) or combination of models and techniques that provides better approximation to the basic
Use of digital computers for correction of gamma method and neutron-gamma method indications
International Nuclear Information System (INIS)
Lakhnyuk, V.M.
The program for the NAIRI-S computer is described which is intended for accounting and elimination of the effect of by-processes when interpreting gamma and neutron-gamma logging indications. By means of slight corrections it is possible to use the program as a mathematical basis for logging diagram standardization by the method of multidimensional regressive analysis and estimation of rock reservoir properties
Directory of Open Access Journals (Sweden)
Umara Firman Rizidansyah
Full Text Available The purpose of this research is to examine suitability of three types of haze correction methods toward distinctness of surface objects in land cover. Considering the formation of haze therefore the main research are divided into both region namely rural assumed as vegetation and urban assumed as non vegetation area. Region of interest for rural selected Balaraja and urban selected Penjaringan. Haze imagery reduction utilized techniques such as Dark Object Substration, Virtual Cloud Point and Histogram Match. By applying an equation of Haze Optimized Transformation HOT = DNbluesin(∂-DNredcos(∂, the main result of this research includes: in the case of AVNIR-Rural, VCP has good results on Band 1 while the HM has good results on band 2, 3 and 4, therefore in the case of Avnir-Rural can be applied to HM. in the case of AVNIR-Urban, DOS has good result on band 1, 2 and 3 meanwhile HM has good results on band 4, therefore in the case of AVNIR-Urban can be applied to DOS. In the case of Landsat-Rural, DOS has good result on band 1, 2 and 6 meanwhile VCP has good results on band 4 and 5 and the smallest average value of HOT is 106.547 by VCP, therefore in the case of Lansat-Rural can be applied to DOS and VCP. In the case of Landsat-Urban, DOS has good result on band 1, 2 and 6 meanwhile VCP has good results on band 3, 4 and 5, therefore in the case of Landsat-Urban can be applied to VCP. Tujuan penelitian ini untuk menguji kesesuaian tiga jenis metode koreksi haze terhadap kejelasan obyek permukaan di wilayah tutupan vegetasi dan non vegetasi, berkenaan menghilangkan haze di wilayah citra satelit optis yang memiliki karakteristik tertentu dan diduga proses pembentukan partikel hazenya berbeda. Sehingga daerah penelitian dibagi menjadi wilayah rural yang diasumsikan sebagai daerah vegetasi dan urban sebagai non vegetasi. Pedesaan terpilih kecamatan Balaraja dan Perkotaan terpilih kecamatan Penjaringan. Tiap lokasi menggunakan Avnir-2 dan Landsat
Proton dose distribution measurements using a MOSFET detector with a simple dose-weighted correction method for LET effects.
Kohno, Ryosuke; Hotta, Kenji; Matsuura, Taeko; Matsubara, Kana; Nishioka, Shie; Nishio, Teiji; Kawashima, Mitsuhiko; Ogino, Takashi
We experimentally evaluated the proton beam dose reproducibility, sensitivity, angular dependence and depth-dose relationships for a new Metal Oxide Semiconductor Field Effect Transistor (MOSFET) detector. The detector was fabricated with a thinner oxide layer and was operated at high-bias voltages. In order to accurately measure dose distributions, we developed a practical method for correcting the MOSFET response to proton beams. The detector was tested by examining lateral dose profiles formed by protons passing through an L-shaped bolus. The dose reproducibility, angular dependence and depth-dose response were evaluated using a 190 MeV proton beam. Depth-output curves produced using the MOSFET detectors were compared with results obtained using an ionization chamber (IC). Since accurate measurements of proton dose distribution require correction for LET effects, we developed a simple dose-weighted correction method. The correction factors were determined as a function of proton penetration depth, or residual range. The residual proton range at each measurement point was calculated using the pencil beam algorithm. Lateral measurements in a phantom were obtained for pristine and SOBP beams. The reproducibility of the MOSFET detector was within 2%, and the angular dependence was less than 9%. The detector exhibited a good response at the Bragg peak (0.74 relative to the IC detector). For dose distributions resulting from protons passing through an L-shaped bolus, the corrected MOSFET dose agreed well with the IC results. Absolute proton dosimetry can be performed using MOSFET detectors to a precision of about 3% (1 sigma). A thinner oxide layer thickness improved the LET in proton dosimetry. By employing correction methods for LET dependence, it is possible to measure absolute proton dose using MOSFET detectors.
Proton dose distribution measurements using a MOSFET detector with a simple dose‐weighted correction method for LET effects
Hotta, Kenji; Matsuura, Taeko; Matsubara, Kana; Nishioka, Shie; Nishio, Teiji; Kawashima, Mitsuhiko; Ogino, Takashi
We experimentally evaluated the proton beam dose reproducibility, sensitivity, angular dependence and depth‐dose relationships for a new Metal Oxide Semiconductor Field Effect Transistor (MOSFET) detector. The detector was fabricated with a thinner oxide layer and was operated at high‐bias voltages. In order to accurately measure dose distributions, we developed a practical method for correcting the MOSFET response to proton beams. The detector was tested by examining lateral dose profiles formed by protons passing through an L‐shaped bolus. The dose reproducibility, angular dependence and depth‐dose response were evaluated using a 190 MeV proton beam. Depth‐output curves produced using the MOSFET detectors were compared with results obtained using an ionization chamber (IC). Since accurate measurements of proton dose distribution require correction for LET effects, we developed a simple dose‐weighted correction method. The correction factors were determined as a function of proton penetration depth, or residual range. The residual proton range at each measurement point was calculated using the pencil beam algorithm. Lateral measurements in a phantom were obtained for pristine and SOBP beams. The reproducibility of the MOSFET detector was within 2%, and the angular dependence was less than 9%. The detector exhibited a good response at the Bragg peak (0.74 relative to the IC detector). For dose distributions resulting from protons passing through an L‐shaped bolus, the corrected MOSFET dose agreed well with the IC results. Absolute proton dosimetry can be performed using MOSFET detectors to a precision of about 3% (1 sigma). A thinner oxide layer thickness improved the LET in proton dosimetry. By employing correction methods for LET dependence, it is possible to measure absolute proton dose using MOSFET detectors. PACS number: 87.56.‐v
A method of estimating GPS instrumental biases with a convolution algorithm
Li, Qi; Ma, Guanyi; Lu, Weijun; Wan, Qingtao; Fan, Jiangtao; Wang, Xiaolan; Li, Jinghua; Li, Changhua
This paper presents a method of deriving the instrumental differential code biases (DCBs) of GPS satellites and dual frequency receivers. Considering that the total electron content (TEC) varies smoothly over a small area, one ionospheric pierce point (IPP) and four more nearby IPPs were selected to build an equation with a convolution algorithm. In addition, unknown DCB parameters were arranged into a set of equations with GPS observations in a day unit by assuming that DCBs do not vary within a day. Then, the DCBs of satellites and receivers were determined by solving the equation set with the least-squares fitting technique. The performance of this method is examined by applying it to 361 days in 2014 using the observation data from 1311 GPS Earth Observation Network (GEONET) receivers. The result was crosswise-compared with the DCB estimated by the mesh method and the IONEX products from the Center for Orbit Determination in Europe (CODE). The DCB values derived by this method agree with those of the mesh method and the CODE products, with biases of 0.091 ns and 0.321 ns, respectively. The convolution method's accuracy and stability were quite good and showed improvements over the mesh method.
New methods for the correction of 31P NMR spectra in in vivo NMR spectroscopy
International Nuclear Information System (INIS)
Starcuk, Z.; Bartusek, K.; Starcuk, Z. jr.
The new methods for the correction of 31 P NMR spectra in vivo NMR spectroscopy have been performed. A method for the baseline correction of the spectra which represents a combination of time-domain and frequency-domain has been discussed.The method is very fast and efficient for minimization of base line artifacts of biological tissues impact
Scatter correction method with primary modulator for dual energy digital radiography: a preliminary study
Jo, Byung-Du; Lee, Young-Jin; Kim, Dae-Hong; Jeon, Pil-Hyun; Kim, Hee-Joung
In conventional digital radiography (DR) using a dual energy subtraction technique, a significant fraction of the detected photons are scattered within the body, resulting in the scatter component. Scattered radiation can significantly deteriorate image quality in diagnostic X-ray imaging systems. Various methods of scatter correction, including both measurement and non-measurement-based methods have been proposed in the past. Both methods can reduce scatter artifacts in images. However, non-measurement-based methods require a homogeneous object and have insufficient scatter component correction. Therefore, we employed a measurement-based method to correct for the scatter component of inhomogeneous objects from dual energy DR (DEDR) images. We performed a simulation study using a Monte Carlo simulation with a primary modulator, which is a measurement-based method for the DEDR system. The primary modulator, which has a checkerboard pattern, was used to modulate primary radiation. Cylindrical phantoms of variable size were used to quantify imaging performance. For scatter estimation, we used Discrete Fourier Transform filtering. The primary modulation method was evaluated using a cylindrical phantom in the DEDR system. The scatter components were accurately removed using a primary modulator. When the results acquired with scatter correction and without correction were compared, the average contrast-to-noise ratio (CNR) with the correction was 1.35 times higher than that obtained without correction, and the average root mean square error (RMSE) with the correction was 38.00% better than that without correction. In the subtraction study, the average CNR with correction was 2.04 (aluminum subtraction) and 1.38 (polymethyl methacrylate (PMMA) subtraction) times higher than that obtained without the correction. The analysis demonstrated the accuracy of scatter correction and the improvement of image quality using a primary modulator and showed the feasibility of
Method and apparatus for optical phase error correction
DeRose, Christopher; Bender, Daniel A.
The phase value of a phase-sensitive optical device, which includes an optical transport region, is modified by laser processing. At least a portion of the optical transport region is exposed to a laser beam such that the phase value is changed from a first phase value to a second phase value, where the second phase value is different from the first phase value. The portion of the optical transport region that is exposed to the laser beam can be a surface of the optical transport region or a portion of the volume of the optical transport region. In an embodiment of the invention, the phase value of the optical device is corrected by laser processing. At least a portion of the optical transport region is exposed to a laser beam until the phase value of the optical device is within a specified tolerance of a target phase value.
Genomes correction and assembling: present methods and tools
Wojcieszek, Michał; Pawełkowicz, Magdalena; Nowak, Robert; Przybecki, Zbigniew
Recent rapid development of next generation sequencing (NGS) technologies provided significant impact into genomics field of study enabling implementation of many de novo sequencing projects of new species which was previously confined by technological costs. Along with advancement of NGS there was need for adjustment in assembly programs. New algorithms must cope with massive amounts of data computation in reasonable time limits and processing power and hardware is also an important factor. In this paper, we address the issue of assembly pipeline for de novo genome assembly provided by programs presently available for scientist both as commercial and as open - source software. The implementation of four different approaches - Greedy, Overlap - Layout - Consensus (OLC), De Bruijn and Integrated resulting in variation of performance is the main focus of our discussion with additional insight into issue of short and long reads correction.
Texture analysis by the Schulz reflection method: Defocalization corrections for thin films
International Nuclear Information System (INIS)
Chateigner, D.; Germi, P.; Pernet, M.
A new method is described for correcting experimental data obtained from the texture analysis of thin films. The analysis employed for correcting the data usually requires the experimental curves of defocalization for a randomly oriented specimen. In view of difficulties in finding non-oriented films, a theoretical method for these corrections is proposed which uses the defocalization evolution for a bulk sample, the film thickness and the penetration depth of the incident beam in the material. This correction method is applied to a film of YBa 2 CU 3 O 7-δ on an SrTiO 3 single-crystal substrate. (orig.)
Band extension in digital methods of transfer function determination – signal conditioners asymmetry error corrections
Directory of Open Access Journals (Sweden)
Zbigniew Staroszczyk
Full Text Available [b]Abstract[/b]. In the paper, the calibrating method for error correction in transfer function determination with the use of DSP has been proposed. The correction limits/eliminates influence of transfer function input/output signal conditioners on the estimated transfer functions in the investigated object. The method exploits frequency domain conditioning paths descriptor found during training observation made on the known reference object.[b]Keywords[/b]: transfer function, band extension, error correction, phase errors
Correction to the method of Talmadge and Fitch
International Nuclear Information System (INIS)
Sincero, A.P.
The method of Talmadge and Fitch used for calculating thickener areas was published in 1955. Although in the United States, this method has largely been superseded by the solids flux method, there are other parts in the world that use this method even up to the present. The method, however, is erroneous and this needs to be known to potential users. The error lies in the assumption that the underflow concentration, C u , and the time of thickening, t u , in a continuous-flow thickener can be obtained from data obtained in a single batch settling test. This paper will show that this assumption is incorrect. (author)
The Use of a Statistical Model of Storm Surge as a Bias Correction for Dynamical Surge Models and its Applicability along the U.S. East Coast
Directory of Open Access Journals (Sweden)
Haydee Salmun
Full Text Available The present study extends the applicability of a statistical model for prediction of storm surge originally developed for The Battery, NY in two ways: I. the statistical model is used as a biascorrection for operationally produced dynamical surge forecasts, and II. the statistical model is applied to the region of the east coast of the U.S. susceptible to winter extratropical storms. The statistical prediction is based on a regression relation between the "storm maximum" storm surge and the storm composite significant wave height predicted ata nearby location. The use of the statistical surge prediction as an alternative bias correction for the National Oceanic and Atmospheric Administration (NOAA operational storm surge forecasts is shownhere to be statistically equivalent to the existing bias correctiontechnique and potentially applicable for much longer forecast lead times as well as for storm surge climate prediction. Applying the statistical model to locations along the east coast shows that the regression relation can be "trained" with data from tide gauge measurements and near-shore buoys along the coast from North Carolina to Maine, and that it provides accurate estimates of storm surge.
A new digitized reverse correction method for hypoid gears based on a one-dimensional probe
Li, Tianxing; Li, Jubo; Deng, Xiaozhong; Yang, Jianjun; Li, Genggeng; Ma, Wensuo
In order to improve the tooth surface geometric accuracy and transmission quality of hypoid gears, a new digitized reverse correction method is proposed based on the measurement data from a one-dimensional probe. The minimization of tooth surface geometrical deviations is realized from the perspective of mathematical analysis and reverse engineering. Combining the analysis of complex tooth surface generation principles and the measurement mechanism of one-dimensional probes, the mathematical relationship between the theoretical designed tooth surface, the actual machined tooth surface and the deviation tooth surface is established, the mapping relation between machine-tool settings and tooth surface deviations is derived, and the essential connection between the accurate calculation of tooth surface deviations and the reverse correction method of machine-tool settings is revealed. Furthermore, a reverse correction model of machine-tool settings is built, a reverse correction strategy is planned, and the minimization of tooth surface deviations is achieved by means of the method of numerical iterative reverse solution. On this basis, a digitized reverse correction system for hypoid gears is developed by the organic combination of numerical control generation, accurate measurement, computer numerical processing, and digitized correction. Finally, the correctness and practicability of the digitized reverse correction method are proved through a reverse correction experiment. The experimental results show that the tooth surface geometric deviations meet the engineering requirements after two trial cuts and one correction.
Analysis and development of methods of correcting for heterogeneities to cobalt-60: computing application
International Nuclear Information System (INIS)
Kappas, K.
The purpose of this work is the analysis of the influence of inhomogeneities of the human body on the determination of the dose in Cobalt-60 radiation therapy. The first part is dedicated to the physical characteristics of inhomogeneities and to the conventional methods of correction. New methods of correction are proposed based on the analysis of the scatter. This analysis allows to take account, with a greater accuracy of their physical characteristics and of the corresponding modifications of the dose: ''the differential TAR method'' and ''the Beam Substraction Method''. The second part is dedicated to the computer implementation of the second method of correction for routine application in hospital [fr
A High Precision Laser-Based Autofocus Method Using Biased Image Plane for Microscopy
Directory of Open Access Journals (Sweden)
Chao-Chen Gu
Full Text Available This study designs and accomplishes a high precision and robust laser-based autofocusing system, in which a biased image plane is applied. In accordance to the designed optics, a cluster-based circle fitting algorithm is proposed to calculate the radius of the detecting spot from the reflected laser beam as an essential factor to obtain the defocus value. The experiment conduct on the experiment device achieved novel performance of high precision and robustness. Furthermore, the low demand of assembly accuracy makes the proposed method a low-cost and realizable solution for autofocusing technique.
Comparison of some biased estimation methods (including ordinary subset regression) in the linear model
Sidik, S. M.
Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.
Comparing Different Approaches of Bias Correction for Ability Estimation in IRT Models. Research Report. ETS RR-08-13
Lee, Yi-Hsuan; Zhang, Jinming
The method of maximum-likelihood is typically applied to item response theory (IRT) models when the ability parameter is estimated while conditioning on the true item parameters. In practice, the item parameters are unknown and need to be estimated first from a calibration sample. Lewis (1985) and Zhang and Lu (2007) proposed the expected response…
"Are cognitive interventions effective in Alzheimer's disease? A controlled meta- analysis of the effects of bias": Correction to Oltra-Cucarella et al. (2016).
Reports an error in "Are Cognitive Interventions Effective in Alzheimer's Disease? A Controlled Meta-Analysis of the Effects of Bias" by Javier Oltra-Cucarella, Rubén Pérez-Elvira, Raul Espert and Anita Sohn McCormick (Neuropsychology, Advanced Online Publication, Apr 7, 2016, np). In the article the first sentence of the third paragraph of the Source of bias subsection in the Statistical Analysis subsection of the Correlational Meta-Analysis section should read "For the control condition bias, three comparison groups were differentiated: (a) a structured cognitive intervention, (b) a placebo control condition, and (c) a pharma control condition without cognitive intervention or no treatment at all." (The following abstract of the original article appeared in record 2016-16656-001.) There is limited evidence about the efficacy of cognitive interventions for Alzheimer's disease (AD). However, aside from the methodological quality of the studies analyzed, the methodology used in previous meta-analyses is itself a risk of bias as different types of effect sizes (ESs) were calculated and combined. This study aimed at examining the results of nonpharmacological interventions for AD with an adequate control of statistical methods and to demonstrate a different approach to meta-analysis. ESs were calculated with the independent groups pre/post design. Average ESs for separate outcomes were calculated and moderator analyses were performed so as to offer an overview of the effects of bias. Eighty-seven outcomes from 19 studies (n = 812) were meta-analyzed. ESs were small on average for cognitive and functional outcomes after intervention. Moderator analyses showed no effect of control of bias, although ESs were different from zero only in some circumstances (e.g., memory outcomes in randomized studies). Cognitive interventions showed no more efficacy than placebo interventions, and functional ESs were consistently low across conditions. cognitive interventions delivered
Evaluation and parameterization of ATCOR3 topographic correction method for forest cover mapping in mountain areas
Balthazar, Vincent; Vanacker, Veerle; Lambin, Eric F.
A topographic correction of optical remote sensing data is necessary to improve the quality of quantitative forest cover change analyses in mountainous terrain. The implementation of semi-empirical correction methods requires the calibration of model parameters that are empirically defined. This study develops a method to improve the performance of topographic corrections for forest cover change detection in mountainous terrain through an iterative tuning method of model parameters based on a systematic evaluation of the performance of the correction. The latter was based on: (i) the general matching of reflectances between sunlit and shaded slopes and (ii) the occurrence of abnormal reflectance values, qualified as statistical outliers, in very low illuminated areas. The method was tested on Landsat ETM+ data for rough (Ecuadorian Andes) and very rough mountainous terrain (Bhutan Himalayas). Compared to a reference level (no topographic correction), the ATCOR3 semi-empirical correction method resulted in a considerable reduction of dissimilarities between reflectance values of forested sites in different topographic orientations. Our results indicate that optimal parameter combinations are depending on the site, sun elevation and azimuth and spectral conditions. We demonstrate that the results of relatively simple topographic correction methods can be greatly improved through a feedback loop between parameter tuning and evaluation of the performance of the correction model.
Research on 3-D terrain correction methods of airborne gamma-ray spectrometry survey
International Nuclear Information System (INIS)
Liu Yanyang; Liu Qingcheng; Zhang Zhiyong
The general method of height correction is not effectual in complex terrain during the process of explaining airborne gamma-ray spectrometry data, and the 2-D terrain correction method researched in recent years is just available for correction of section measured. A new method of 3-D sector terrain correction is studied. The ground radiator is divided into many small sector radiators by the method, then the irradiation rate is calculated in certain survey distance, and the total value of all small radiate sources is regarded as the irradiation rate of the ground radiator at certain point of aero- survey, and the correction coefficients of every point are calculated which then applied to correct to airborne gamma-ray spectrometry data. The method can achieve the forward calculation, inversion calculation and terrain correction for airborne gamma-ray spectrometry survey in complex topography by dividing the ground radiator into many small sectors. Other factors are considered such as the un- saturated degree of measure scope, uneven-radiator content on ground, and so on. The results of for- ward model and an example analysis show that the 3-D terrain correction method is proper and effectual. (authors)
Study protocol: the empirical investigation of methods to correct for measurement error in biobanks with dietary assessment
Directory of Open Access Journals (Sweden)
Masson Lindsey F
Full Text Available Abstract Background The Public Population Project in Genomics (P3G is an organisation that aims to promote collaboration between researchers in the field of population-based genomics. The main objectives of P3G are to encourage collaboration between researchers and biobankers, optimize study design, promote the harmonization of information use in biobanks, and facilitate transfer of knowledge between interested parties. The importance of calibration and harmonisation of methods for environmental exposure assessment to allow pooling of data across studies in the evaluation of gene-environment interactions has been recognised by P3G, which has set up a methodological group on calibration with the aim of; 1 reviewing the published methodological literature on measurement error correction methods with assumptions and methods of implementation; 2 reviewing the evidence available from published nutritional epidemiological studies that have used a calibration approach; 3 disseminating information in the form of a comparison chart on approaches to perform calibration studies and how to obtain correction factors in order to support research groups collaborating within the P3G network that are unfamiliar with the methods employed; 4 with application to the field of nutritional epidemiology, including gene-diet interactions, ultimately developing a inventory of the typical correction factors for various nutrients. Methods/Design Systematic review of (a the methodological literature on methods to correct for measurement error in epidemiological studies; and (b studies that have been designed primarily to investigate the association between diet and disease and have also corrected for measurement error in dietary intake. Discussion The conduct of a systematic review of the methodological literature on calibration will facilitate the evaluation of methods to correct for measurement error and the design of calibration studies for the prospective pooling of
[The importance of memory bias in obtaining age of menarche by recall method in Brazilian adolescents].
Castilho, Silvia Diez; Nucci, Luciana Bertoldi; Assuino, Samanta Ramos; Hansen, Lucca Ortolan
To compare the age at menarche obtained by recall method according to the time elapsed since the event, in order to verify the importance of the recall bias. Were evaluated 1,671 girls (7-18 years) at schools in Campinas-SP regarding the occurrence of menarche by the status quo method (menarche: yes or no) and the recall method (date of menarche, for those who mentioned it). The age at menarche obtained by the status quo method was calculated by logit, which considers the whole group, and the age obtained by the recall method was calculated as the average of the mentioned age at menarche. In this group, the age at menarche was obtained by the difference between the date of the event and the date of birth. Girls who reported menarche (883, 52.8%) were divided into four groups according to the time elapsed since the event. To analyze the results, we used ANOVA and logistic regression for the analysis, with a significance level of 0.05. The age at menarche calculated by logit was 12.14 y/o (95% CI 12.08 to 12.20). Mean ages obtained by recall were: for those who experienced menarche within the previous year 12.26 y/o (±1.14), between > 1-2 years before, 12.29 y (±1.22); between > 2-3 years before, 12.23 y/o (±1.27); and more than 3 years before, 11.55y/o (±1.24), p recall method was similar for girls who menstruated within the previous 3 years (and approaches the age calculated by logit); when more than 3 years have passed, the recall bias was significant.
Biomedical journals lack a consistent method to detect outcome reporting bias: a cross-sectional analysis.
Huan, L N; Tejani, A M; Egan, G
An increasing amount of recently published literature has implicated outcome reporting bias (ORB) as a major contributor to skewing data in both randomized controlled trials and systematic reviews; however, little is known about the current methods in place to detect ORB. This study aims to gain insight into the detection and management of ORB by biomedical journals. This was a cross-sectional analysis involving standardized questions via email or telephone with the top 30 biomedical journals (2012) ranked by impact factor. The Cochrane Database of Systematic Reviews was excluded leaving 29 journals in the sample. Of 29 journals, 24 (83%) responded to our initial inquiry of which 14 (58%) answered our questions and 10 (42%) declined participation. Five (36%) of the responding journals indicated they had a specific method to detect ORB, whereas 9 (64%) did not have a specific method in place. The prevalence of ORB in the review process seemed to differ with 4 (29%) journals indicating ORB was found commonly, whereas 7 (50%) indicated ORB was uncommon or never detected by their journal previously. The majority (n = 10/14, 72%) of journals were unwilling to report or make discrepancies found in manuscripts available to the public. Although the minority, there were some journals (n = 4/14, 29%) which described thorough methods to detect ORB. Many journals seemed to lack a method with which to detect ORB and its estimated prevalence was much lower than that reported in literature suggesting inadequate detection. There exists a potential for overestimation of treatment effects of interventions and unclear risks. Fortunately, there are journals within this sample which appear to utilize comprehensive methods for detection of ORB, but overall, the data suggest improvements at the biomedical journal level for detecting and minimizing the effect of this bias are needed. © 2014 John Wiley & Sons Ltd.
Development of a practical image-based scatter correction method for brain perfusion SPECT: comparison with the TEW method
International Nuclear Information System (INIS)
Shidahara, Miho; Kato, Takashi; Kawatsu, Shoji; Yoshimura, Kumiko; Ito, Kengo; Watabe, Hiroshi; Kim, Kyeong Min; Iida, Hidehiro; Kato, Rikio
An image-based scatter correction (IBSC) method was developed to convert scatter-uncorrected into scatter-corrected SPECT images. The purpose of this study was to validate this method by means of phantom simulations and human studies with 99m Tc-labeled tracers, based on comparison with the conventional triple energy window (TEW) method. The IBSC method corrects scatter on the reconstructed image I AC μb with Chang's attenuation correction factor. The scatter component image is estimated by convolving I AC μb with a scatter function followed by multiplication with an image-based scatter fraction function. The IBSC method was evaluated with Monte Carlo simulations and 99m Tc-ethyl cysteinate dimer SPECT human brain perfusion studies obtained from five volunteers. The image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were compared. Using data obtained from the simulations, the image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were found to be nearly identical for both gray and white matter. In human brain images, no significant differences in image contrast were observed between the IBSC and TEW methods. The IBSC method is a simple scatter correction technique feasible for use in clinical routine. (orig.)
Development of a practical image-based scatter correction method for brain perfusion SPECT: comparison with the TEW method
Energy Technology Data Exchange (ETDEWEB)
Shidahara, Miho; Kato, Takashi; Kawatsu, Shoji; Yoshimura, Kumiko; Ito, Kengo [National Center for Geriatrics and Gerontology Research Institute, Department of Brain Science and Molecular Imaging, Obu, Aichi (Japan); Watabe, Hiroshi; Kim, Kyeong Min; Iida, Hidehiro [National Cardiovascular Center Research Institute, Department of Investigative Radiology, Suita (Japan); Kato, Rikio [National Center for Geriatrics and Gerontology, Department of Radiology, Obu (Japan)
An image-based scatter correction (IBSC) method was developed to convert scatter-uncorrected into scatter-corrected SPECT images. The purpose of this study was to validate this method by means of phantom simulations and human studies with {sup 99m}Tc-labeled tracers, based on comparison with the conventional triple energy window (TEW) method. The IBSC method corrects scatter on the reconstructed image I{sub AC}{sup {mu}}{sup b} with Chang's attenuation correction factor. The scatter component image is estimated by convolving I{sub AC}{sup {mu}}{sup b} with a scatter function followed by multiplication with an image-based scatter fraction function. The IBSC method was evaluated with Monte Carlo simulations and {sup 99m}Tc-ethyl cysteinate dimer SPECT human brain perfusion studies obtained from five volunteers. The image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were compared. Using data obtained from the simulations, the image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were found to be nearly identical for both gray and white matter. In human brain images, no significant differences in image contrast were observed between the IBSC and TEW methods. The IBSC method is a simple scatter correction technique feasible for use in clinical routine. (orig.)
Development of a practical image-based scatter correction method for brain perfusion SPECT: comparison with the TEW method.
Shidahara, Miho; Watabe, Hiroshi; Kim, Kyeong Min; Kato, Takashi; Kawatsu, Shoji; Kato, Rikio; Yoshimura, Kumiko; Iida, Hidehiro; Ito, Kengo
An image-based scatter correction (IBSC) method was developed to convert scatter-uncorrected into scatter-corrected SPECT images. The purpose of this study was to validate this method by means of phantom simulations and human studies with 99mTc-labeled tracers, based on comparison with the conventional triple energy window (TEW) method. The IBSC method corrects scatter on the reconstructed image I(mub)AC with Chang's attenuation correction factor. The scatter component image is estimated by convolving I(mub)AC with a scatter function followed by multiplication with an image-based scatter fraction function. The IBSC method was evaluated with Monte Carlo simulations and 99mTc-ethyl cysteinate dimer SPECT human brain perfusion studies obtained from five volunteers. The image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were compared. Using data obtained from the simulations, the image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were found to be nearly identical for both gray and white matter. In human brain images, no significant differences in image contrast were observed between the IBSC and TEW methods. The IBSC method is a simple scatter correction technique feasible for use in clinical routine.
Methods for Motion Correction Evaluation Using 18F-FDG Human Brain Scans on a High-Resolution PET Scanner
DEFF Research Database (Denmark)
Keller, Sune H.; Sibomana, Merence; Olesen, Oline Vinter
Many authors have reported the importance of motion correction (MC) for PET. Patient motion during scanning disturbs kinetic analysis and degrades resolution. In addition, using misaligned transmission for attenuation and scatter correction may produce regional quantification bias in the reconstr......Many authors have reported the importance of motion correction (MC) for PET. Patient motion during scanning disturbs kinetic analysis and degrades resolution. In addition, using misaligned transmission for attenuation and scatter correction may produce regional quantification bias...... in the reconstructed emission images. The purpose of this work was the development of quality control (QC) methods for MC procedures based on external motion tracking (EMT) for human scanning using an optical motion tracking system. Methods: Two scans with minor motion and 5 with major motion (as reported...... (automated image registration) software. The following 3 QC methods were used to evaluate the EMT and AIR MC: a method using the ratio between 2 regions of interest with gray matter voxels (GM) and white matter voxels (WM), called GM/WM; mutual information; and cross correlation. Results: The results...
Comparison of classical methods for blade design and the influence of tip correction on rotor performance
DEFF Research Database (Denmark)
Sørensen, Jens Nørkær; Okulov, Valery; Mikkelsen, Robert Flemming
The classical blade-element/momentum (BE/M) method, which is used together with different types of corrections (e.g. the Prandtl or Glauert tip correction), is today the most basic tool in the design of wind turbine rotors. However, there are other classical techniques based on a combination...
Application of pulse pile-up correction spectrum to the library least-squares method
Energy Technology Data Exchange (ETDEWEB)
Lee, Sang Hoon [Kyungpook National Univ., Daegu (Korea, Republic of)
The Monte Carlo simulation code CEARPPU has been developed and updated to provide pulse pile-up correction spectra for high counting rate cases. For neutron activation analysis, CEARPPU correction spectra were used in library least-squares method to give better isotopic activity results than the convention library least-squares fitting with uncorrected spectra.
Resistivity Correction Factor for the Four-Probe Method: Experiment I
Yamashita, Masato; Yamaguchi, Shoji; Enjoji, Hideo
Experimental verification of the theoretically derived resistivity correction factor (RCF) is presented. Resistivity and sheet resistance measurements by the four-probe method are made on three samples: isotropic graphite, ITO film and Au film. It is indicated that the RCF can correct the apparent variations of experimental data to yield reasonable resistivities and sheet resistances.
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
Fornasier, Massimo
This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via oblique thresholding, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for degenerate elliptic PDEs arising in total variation minimization and in accelerated sparse recovery algorithms based on 1-minimization. We include numerical examples which show e.cient solutions to classical problems in signal and image processing. © 2009 Society for Industrial and Applied Physics.
Methodical bias for comparison of periodontal ligament injection and local infiltration anesthesia for routine extractions in the maxilla
Directory of Open Access Journals (Sweden)
Kämmerer PW
Full Text Available Peer W Kämmerer, Monika Daubländer Department of Oral, Maxillofacial and Facial Plastic Surgery, University Medical Centre Mainz, Mainz, GermanyWe read the article by Al-Shayyab1 with great interest, though we think that there is a methodical bias. Usage of standard dental syringes with 27-gauge needles is not recommended for periodontal ligament (PDL injections as they are very unlikely to achieve the correct pressure needed for successful single tooth anesthesia. In accordance with this, specialized syringes with short 30-gauge needles are commonly used all over the literature.2 The author addresses this in the "Discussion" section and writes that "a standard conventional dental syringe was used in the present study, not a special PDL syringe, since the former is readily available in the clinic and proves equally successful when a standard 27-gauge short needle was used," citing Malamed from 1982 (a time during which the modern PDL syringes were not developed yet3 and Madan et al who write that "intraligamentary injection technique is equally effective when a standard 27-gauge needle is used".4 The second assumption refers to the needle only, not the syringe. In addition, this needle issue is not proven by any reference or study. Therefore, one might come to the conclusion that PDL was not carried out correctly. Also, the authors did not evaluate pulp or tissue anesthesia and started the extraction procedure after a latency period of 5 minutes in all cases. In accordance with this, the success rates of the PDL injection cannot be given, but would be of interest.View the original paper by Al-Shayyab and colleagues.
Propensity score matching and persistence correction to reduce bias in comparative effectiveness: the effect of cinacalcet use on all-cause mortality.
Gillespie, Iain A; Floege, Jürgen; Gioni, Ioanna; Drüeke, Tilman B; de Francisco, Angel L; Anker, Stefan D; Kubo, Yumi; Wheeler, David C; Froissart, Marc
The generalisability of randomised controlled trials (RCTs) may be limited by restrictive entry criteria or by their experimental nature. Observational research can provide complementary findings but is prone to bias. Employing propensity score matching, to reduce such bias, we compared the real-life effect of cinacalcet use on all-cause mortality (ACM) with findings from the Evaluation of Cinacalcet Therapy to Lower Cardiovascular Events (EVOLVE) RCT in chronic haemodialysis patients. Incident adult haemodialysis patients receiving cinacalcet, recruited in a prospective observational cohort from 2007-2009 (AROii; n = 10,488), were matched to non-exposed patients regardless of future exposure status. The effect of treatment crossover was investigated with inverse probability of censoring weighted and lag-censored analyses. EVOLVE ACM data were analysed largely as described for the primary composite endpoint. AROii patients receiving cinacalcet (n = 532) were matched to 1790 non-exposed patients. The treatment effect of cinacalcet on ACM in the main AROii analysis (hazard ratio 1.03 [95% confidence interval (CI) 0.78-1.35]) was closer to the null than for the Intention to Treat (ITT) analysis of EVOLVE (0.94 [95%CI 0.85-1.04]). Adjusting for non-persistence by 0- and 6-month lag-censoring and by inverse probability of censoring weight, the hazard ratios in AROii (0.76 [95%CI 0.51-1.15], 0.84 [95%CI 0.60-1.18] and 0.79 [95%CI 0.56-1.11], respectively) were comparable with those of EVOLVE (0.82 [95%CI 0.67-1.01], 0.83 [95%CI 0.73-0.96] and 0.87 [95%CI 0.71-1.06], respectively). Correcting for treatment crossover, we observed results in the 'real-life' setting of the AROii observational cohort that closely mirrored the results of the EVOLVE RCT. Persistence-corrected analyses revealed a trend towards reduced ACM in haemodialysis patients receiving cinacalcet therapy. Copyright © 2015 John Wiley & Sons, Ltd.
Codon usage bias: causative factors, quantification methods and genome-wide patterns: with emphasis on insect genomes.
Behura, Susanta K; Severson, David W
Codon usage bias refers to the phenomenon where specific codons are used more often than other synonymous codons during translation of genes, the extent of which varies within and among species. Molecular evolutionary investigations suggest that codon bias is manifested as a result of balance between mutational and translational selection of such genes and that this phenomenon is widespread across species and may contribute to genome evolution in a significant manner. With the advent of whole-genome sequencing of numerous species, both prokaryotes and eukaryotes, genome-wide patterns of codon bias are emerging in different organisms. Various factors such as expression level, GC content, recombination rates, RNA stability, codon position, gene length and others (including environmental stress and population size) can influence codon usage bias within and among species. Moreover, there has been a continuous quest towards developing new concepts and tools to measure the extent of codon usage bias of genes. In this review, we outline the fundamental concepts of evolution of the genetic code, discuss various factors that may influence biased usage of synonymous codons and then outline different principles and methods of measurement of codon usage bias. Finally, we discuss selected studies performed using whole-genome sequences of different insect species to show how codon bias patterns vary within and among genomes. We conclude with generalized remarks on specific emerging aspects of codon bias studies and highlight the recent explosion of genome-sequencing efforts on arthropods (such as twelve Drosophila species, species of ants, honeybee, Nasonia and Anopheles mosquitoes as well as the recent launch of a genome-sequencing project involving 5000 insects and other arthropods) that may help us to understand better the evolution of codon bias and its biological significance. © 2012 The Authors. Biological Reviews © 2012 Cambridge Philosophical Society.
Hydrogen isotope correction for laser instrument measurement bias at low water vapor concentration using conventional isotope analyses: application to measurements from Mauna Loa Observatory, Hawaii.
Johnson, L R; Sharp, Z D; Galewsky, J; Strong, M; Van Pelt, A D; Dong, F; Noone, D
The hydrogen and oxygen isotope ratios of water vapor can be measured with commercially available laser spectroscopy analyzers in real time. Operation of the laser systems in relatively dry air is difficult because measurements are non-linear as a function of humidity at low water concentrations. Here we use field-based sampling coupled with traditional mass spectrometry techniques for assessing linearity and calibrating laser spectroscopy systems at low water vapor concentrations. Air samples are collected in an evacuated 2 L glass flask and the water is separated from the non-condensable gases cryogenically. Approximately 2 µL of water are reduced to H(2) gas and measured on an isotope ratio mass spectrometer. In a field experiment at the Mauna Loa Observatory (MLO), we ran Picarro and Los Gatos Research (LGR) laser analyzers for a period of 25 days in addition to periodic sample collection in evacuated flasks. When the two laser systems are corrected to the flask data, they are strongly coincident over the entire 25 days. The δ(2)H values were found to change by over 200‰ over 2.5 min as the boundary layer elevation changed relative to MLO. The δ(2)H values ranged from -106 to -332‰, and the δ(18)O values (uncorrected) ranged from -12 to -50‰. Raw data from laser analyzers in environments with low water vapor concentrations can be normalized to the international V-SMOW scale by calibration to the flask data measured conventionally. Bias correction is especially critical for the accurate determination of deuterium excess in dry air. Copyright © 2011 John Wiley & Sons, Ltd.
Evaluation of Fresnel's corrections to the eikonal approximation by the separabilization method
International Nuclear Information System (INIS)
Musakhanov, M.M.; Zubarev, A.L.
Method of separabilization of potential over the Schroedinger approximate solutions, leading to Schwinger's variational principle for scattering amplitude, is suggested. The results are applied to calculation of the Fresnel corrections to the Glauber approximation
A software-based x-ray scatter correction method for breast tomosynthesis
Jia Feng, Steve Si; Sechopoulos, Ioannis
Purpose: To develop a software-based scatter correction method for digital breast tomosynthesis (DBT) imaging and investigate its impact on the image quality of tomosynthesis reconstructions of both phantoms and patients.
Discussion on Boiler Efficiency Correction Method with Low Temperature Economizer-Air Heater System
Ke, Liu; Xing-sen, Yang; Fan-jun, Hou; Zhi-hong, Hu
This paper pointed out that it is wrong to take the outlet flue gas temperature of low temperature economizer as exhaust gas temperature in boiler efficiency calculation based on GB10184-1988. What's more, this paper proposed a new correction method, which decomposed low temperature economizer-air heater system into two hypothetical parts of air preheater and pre condensed water heater and take the outlet equivalent gas temperature of air preheater as exhaust gas temperature in boiler efficiency calculation. This method makes the boiler efficiency calculation more concise, with no air heater correction. It has a positive reference value to deal with this kind of problem correctly.
Error analysis of motion correction method for laser scanning of moving objects
Goel, S.; Lohani, B.
The limitation of conventional laser scanning methods is that the objects being scanned should be static. The need of scanning moving objects has resulted in the development of new methods capable of generating correct 3D geometry of moving objects. Limited literature is available showing development of very few methods capable of catering to the problem of object motion during scanning. All the existing methods utilize their own models or sensors. Any studies on error modelling or analysis of any of the motion correction methods are found to be lacking in literature. In this paper, we develop the error budget and present the analysis of one such `motion correction' method. This method assumes availability of position and orientation information of the moving object which in general can be obtained by installing a POS system on board or by use of some tracking devices. It then uses this information along with laser scanner data to apply correction to laser data, thus resulting in correct geometry despite the object being mobile during scanning. The major application of this method lie in the shipping industry to scan ships either moving or parked in the sea and to scan other objects like hot air balloons or aerostats. It is to be noted that the other methods of "motion correction" explained in literature can not be applied to scan the objects mentioned here making the chosen method quite unique. This paper presents some interesting insights in to the functioning of "motion correction" method as well as a detailed account of the behavior and variation of the error due to different sensor components alone and in combination with each other. The analysis can be used to obtain insights in to optimal utilization of available components for achieving the best results.
Correction of Interferometric and Vegetation Biases in the SRTMGL1 Spaceborne DEM with Hydrological Conditioning towards Improved Hydrodynamics Modeling in the Amazon Basin
Directory of Open Access Journals (Sweden)
Sebastien Pinel
Full Text Available In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1 remains the best topographic information for hydrological and hydrodynamic modeling purposes. However, its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous efforts to remove the vegetation signal either did not account for its spatial variability or relied on a single assumed percentage of penetration of the SRTM signal. Here, we propose a systematic approach over an Amazonian floodplain to remove the vegetation signal, addressing its heterogeneity by combining estimates of vegetation height and a land cover map. We improve this approach by interpolating the first results with drainage network, field and altimetry data to obtain a hydrological conditioned DEM. The averaged interferometric and vegetation biases over the forest zone were found to be −2.0 m and 7.4 m, respectively. Comparing the original and corrected DEM, vertical validation against Ground Control Points shows a RMSE reduction of 64%. Flood extent accuracy, controlled against Landsat and JERS-1 images, stresses improvements in low and high water periods (+24% and +18%, respectively. This study also highlights that a ground truth drainage network, as a unique input during the interpolation, achieves reasonable results in terms of flood extent and hydrological characteristics.
Comparison of Relative Bias, Precision, and Efficiency of Sampling Methods for Natural Enemies of Soybean Aphid (Hemiptera: Aphididae).
Bannerman, J A; Costamagna, A C; McCornack, B P; Ragsdale, D W
Generalist natural enemies play an important role in controlling soybean aphid, Aphis glycines (Hemiptera: Aphididae), in North America. Several sampling methods are used to monitor natural enemy populations in soybean, but there has been little work investigating their relative bias, precision, and efficiency. We compare five sampling methods: quadrats, whole-plant counts, sweep-netting, walking transects, and yellow sticky cards to determine the most practical methods for sampling the three most prominent species, which included Harmonia axyridis (Pallas), Coccinella septempunctata L. (Coleoptera: Coccinellidae), and Orius insidiosus (Say) (Hemiptera: Anthocoridae). We show an important time by sampling method interaction indicated by diverging community similarities within and between sampling methods as the growing season progressed. Similarly, correlations between sampling methods for the three most abundant species over multiple time periods indicated differences in relative bias between sampling methods and suggests that bias is not consistent throughout the growing season, particularly for sticky cards and whole-plant samples. Furthermore, we show that sticky cards produce strongly biased capture rates relative to the other four sampling methods. Precision and efficiency differed between sampling methods and sticky cards produced the most precise (but highly biased) results for adult natural enemies, while walking transects and whole-plant counts were the most efficient methods for detecting coccinellids and O. insidiosus, respectively. Based on bias, precision, and efficiency considerations, the most practical sampling methods for monitoring in soybean include walking transects for coccinellid detection and whole-plant counts for detection of small predators like O. insidiosus. Sweep-netting and quadrat samples are also useful for some applications, when efficiency is not paramount. © The Authors 2015. Published by Oxford University Press on behalf of
Theoretical study on new bias factor methods to effectively use critical experiments for improvement of prediction accuracy of neutronic characteristics
International Nuclear Information System (INIS)
Kugo, Teruhiko; Mori, Takamasa; Takeda, Toshikazu
Extended bias factor methods are proposed with two new concepts, the LC method and the PE method, in order to effectively use critical experiments and to enhance the applicability of the bias factor method for the improvement of the prediction accuracy of neutronic characteristics of a target core. Both methods utilize a number of critical experimental results and produce a semifictitious experimental value with them. The LC and PE methods define the semifictitious experimental values by a linear combination of experimental values and the product of exponentiated experimental values, respectively, and the corresponding semifictitious calculation values by those of calculation values. A bias factor is defined by the ratio of the semifictitious experimental value to the semifictitious calculation value in both methods. We formulate how to determine weights for the LC method and exponents for the PE method in order to minimize the variance of the design prediction value obtained by multiplying the design calculation value by the bias factor. From a theoretical comparison of these new methods with the conventional method which utilizes a single experimental result and the generalized bias factor method which was previously proposed to utilize a number of experimental results, it is concluded that the PE method is the most useful method for improving the prediction accuracy. The main advantages of the PE method are summarized as follows. The prediction accuracy is necessarily improved compared with the design calculation value even when experimental results include large experimental errors. This is a special feature that the other methods do not have. The prediction accuracy is most effectively improved by utilizing all the experimental results. From these facts, it can be said that the PE method effectively utilizes all the experimental results and has a possibility to make a full-scale-mockup experiment unnecessary with the use of existing and future benchmark
A new correction method for determination on carbohydrates in lignocellulosic biomass.
Li, Hong-Qiang; Xu, Jian
The accurate determination on the key components in lignocellulosic biomass is the premise of pretreatment and bioconversion. Currently, the widely used 72% H2SO4 two-step hydrolysis quantitative saccharification (QS) procedure uses loss coefficient of monosaccharide standards to correct monosaccharide loss in the secondary hydrolysis (SH) of QS and may result in excessive correction. By studying the quantitative relationships of glucose and xylose losses during special hydrolysis conditions and the HMF and furfural productions, a simple correction on the monosaccharide loss from both PH and SH was established by using HMF and furfural as the calibrators. This method was used to the component determination on corn stover, Miscanthus and cotton stalk (raw materials and pretreated) and compared to the NREL method. It has been proved that this method can avoid excessive correction on the samples with high-carbohydrate contents. Copyright © 2013 Elsevier Ltd. All rights reserved.
Beam-Based Error Identification and Correction Methods for Particle Accelerators
AUTHOR|(SzGeCERN)692826; Tomas, Rogelio; Nilsson, Thomas
Modern particle accelerators have tight tolerances on the acceptable deviation from their desired machine parameters. The control of the parameters is of crucial importance for safe machine operation and performance. This thesis focuses on beam-based methods and algorithms to identify and correct errors in particle accelerators. The optics measurements and corrections of the Large Hadron Collider (LHC), which resulted in an unprecedented low β-beat for a hadron collider is described. The transverse coupling is another parameter which is of importance to control. Improvement in the reconstruction of the coupling from turn-by-turn data has resulted in a significant decrease of the measurement uncertainty. An automatic coupling correction method, which is based on the injected beam oscillations, has been successfully used in normal operation of the LHC. Furthermore, a new method to measure and correct chromatic coupling that was applied to the LHC, is described. It resulted in a decrease of the chromatic coupli...
Assessment of cognitive bias in decision-making and leadership styles among critical care nurses: a mixed methods study.
Lean Keng, Soon; AlQudah, Hani Nawaf Ibrahim
To raise awareness of critical care nurses' cognitive bias in decision-making, its relationship with leadership styles and its impact on care delivery. The relationship between critical care nurses' decision-making and leadership styles in hospitals has been widely studied, but the influence of cognitive bias on decision-making and leadership styles in critical care environments remains poorly understood, particularly in Jordan. Two-phase mixed methods sequential explanatory design and grounded theory. critical care unit, Prince Hamza Hospital, Jordan. Participant sampling: convenience sampling Phase 1 (quantitative, n = 96), purposive sampling Phase 2 (qualitative, n = 20). Pilot tested quantitative survey of 96 critical care nurses in 2012. Qualitative in-depth interviews, informed by quantitative results, with 20 critical care nurses in 2013. Descriptive and simple linear regression quantitative data analyses. Thematic (constant comparative) qualitative data analysis. Quantitative - correlations found between rationality and cognitive bias, rationality and task-oriented leadership styles, cognitive bias and democratic communication styles and cognitive bias and task-oriented leadership styles. Qualitative - 'being competent', 'organizational structures', 'feeling self-confident' and 'being supported' in the work environment identified as key factors influencing critical care nurses' cognitive bias in decision-making and leadership styles. Two-way impact (strengthening and weakening) of cognitive bias in decision-making and leadership styles on critical care nurses' practice performance. There is a need to heighten critical care nurses' consciousness of cognitive bias in decision-making and leadership styles and its impact and to develop organization-level strategies to increase non-biased decision-making. © 2016 John Wiley & Sons Ltd.
The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network
Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.
The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.
A novel 3D absorption correction method for quantitative EDX-STEM tomography
International Nuclear Information System (INIS)
Burdet, Pierre; Saghi, Z.; Filippin, A.N.; Borrás, A.; Midgley, P.A.
This paper presents a novel 3D method to correct for absorption in energy dispersive X-ray (EDX) microanalysis of heterogeneous samples of unknown structure and composition. By using STEM-based tomography coupled with EDX, an initial 3D reconstruction is used to extract the location of generated X-rays as well as the X-ray path through the sample to the surface. The absorption correction needed to retrieve the generated X-ray intensity is then calculated voxel-by-voxel estimating the different compositions encountered by the X-ray. The method is applied to a core/shell nanowire containing carbon and oxygen, two elements generating highly absorbed low energy X-rays. Absorption is shown to cause major reconstruction artefacts, in the form of an incomplete recovery of the oxide and an erroneous presence of carbon in the shell. By applying the correction method, these artefacts are greatly reduced. The accuracy of the method is assessed using reference X-ray lines with low absorption. - Highlights: • A novel 3D absorption correction method is proposed for 3D EDX-STEM tomography. • The absorption of X-rays along the path to the surface is calculated voxel-by-voxel. • The method is applied on highly absorbed X-rays emitted from a core/shell nanowire. • Absorption is shown to cause major artefacts in the reconstruction. • Using the absorption correction method, the reconstruction artefacts are greatly reduced.
A novel 3D absorption correction method for quantitative EDX-STEM tomography
Energy Technology Data Exchange (ETDEWEB)
Burdet, Pierre, E-mail: firstname.lastname@example.com [Department of Materials Science and Metallurgy, University of Cambridge, Charles Babbage Road 27, Cambridge CB3 0FS, Cambridgeshire (United Kingdom); Saghi, Z. [Department of Materials Science and Metallurgy, University of Cambridge, Charles Babbage Road 27, Cambridge CB3 0FS, Cambridgeshire (United Kingdom); Filippin, A.N.; Borrás, A. [Nanotechnology on Surfaces Laboratory, Materials Science Institute of Seville (ICMS), CSIC-University of Seville, C/ Americo Vespucio 49, 41092 Seville (Spain); Midgley, P.A. [Department of Materials Science and Metallurgy, University of Cambridge, Charles Babbage Road 27, Cambridge CB3 0FS, Cambridgeshire (United Kingdom)
This paper presents a novel 3D method to correct for absorption in energy dispersive X-ray (EDX) microanalysis of heterogeneous samples of unknown structure and composition. By using STEM-based tomography coupled with EDX, an initial 3D reconstruction is used to extract the location of generated X-rays as well as the X-ray path through the sample to the surface. The absorption correction needed to retrieve the generated X-ray intensity is then calculated voxel-by-voxel estimating the different compositions encountered by the X-ray. The method is applied to a core/shell nanowire containing carbon and oxygen, two elements generating highly absorbed low energy X-rays. Absorption is shown to cause major reconstruction artefacts, in the form of an incomplete recovery of the oxide and an erroneous presence of carbon in the shell. By applying the correction method, these artefacts are greatly reduced. The accuracy of the method is assessed using reference X-ray lines with low absorption. - Highlights: • A novel 3D absorption correction method is proposed for 3D EDX-STEM tomography. • The absorption of X-rays along the path to the surface is calculated voxel-by-voxel. • The method is applied on highly absorbed X-rays emitted from a core/shell nanowire. • Absorption is shown to cause major artefacts in the reconstruction. • Using the absorption correction method, the reconstruction artefacts are greatly reduced.
Assessing total nitrogen in surface-water samples--precision and bias of analytical and computational methods
Rus, David L.; Patton, Charles J.; Mueller, David K.; Crawford, Charles G.
The characterization of total-nitrogen (TN) concentrations is an important component of many surface-water-quality programs. However, three widely used methods for the determination of total nitrogen—(1) derived from the alkaline-persulfate digestion of whole-water samples (TN-A); (2) calculated as the sum of total Kjeldahl nitrogen and dissolved nitrate plus nitrite (TN-K); and (3) calculated as the sum of dissolved nitrogen and particulate nitrogen (TN-C)—all include inherent limitations. A digestion process is intended to convert multiple species of nitrogen that are present in the sample into one measureable species, but this process may introduce bias. TN-A results can be negatively biased in the presence of suspended sediment, and TN-K data can be positively biased in the presence of elevated nitrate because some nitrate is reduced to ammonia and is therefore counted twice in the computation of total nitrogen. Furthermore, TN-C may not be subject to bias but is comparatively imprecise. In this study, the effects of suspended-sediment and nitrate concentrations on the performance of these TN methods were assessed using synthetic samples developed in a laboratory as well as a series of stream samples. A 2007 laboratory experiment measured TN-A and TN-K in nutrient-fortified solutions that had been mixed with varying amounts of sediment-reference materials. This experiment identified a connection between suspended sediment and negative bias in TN-A and detected positive bias in TN-K in the presence of elevated nitrate. A 2009–10 synoptic-field study used samples from 77 stream-sampling sites to confirm that these biases were present in the field samples and evaluated the precision and bias of TN methods. The precision of TN-C and TN-K depended on the precision and relative amounts of the TN-component species used in their respective TN computations. Particulate nitrogen had an average variability (as determined by the relative standard deviation) of 13
Methods of correction of carriage of junior schoolchildren by facilities of physical exercises
Directory of Open Access Journals (Sweden)
Gagara V.F.
Full Text Available The results of influence of methods of physical rehabilitation on the organism of children are resulted. In research took part 16 children of lower school with the scoliotic changes of pectoral department of spine. The complex of methods of physical rehabilitation included special correction and general health-improving exercises, medical gymnastics, correction position. Employments on a medical gymnastics during 30-45 minutes 3-4 times per a week were conducted. The improvement of indexes of mobility of spine and state of carriage of schoolchildren is marked. The absolute indexes of the state of carriage and flexibility of spine considerably got around physiology sizes. A rehabilitation complex which includes the elements of correction gymnastics is recommended, medical physical culture, correction, massage of muscles of trunk, position. It is also necessary to adhere to the rational mode of day and feed, provide the normative parameters of working furniture and self-control of the state of carriage.
An efficient shutter-less non-uniformity correction method for infrared focal plane arrays
Huang, Xiyan; Sui, Xiubao; Zhao, Yao
The non-uniformity response in infrared focal plane array (IRFPA) detectors has a bad effect on images with fixed pattern noise. At present, it is common to use shutter to prevent from radiation of target and to update the parameters of non-uniformity correction in the infrared imaging system. The use of shutter causes "freezing" image. And inevitably, there exists the problems of the instability and reliability of system, power consumption, and concealment of infrared detection. In this paper, we present an efficient shutter-less non-uniformity correction (NUC) method for infrared focal plane arrays. The infrared imaging system can use the data gaining in thermostat to calculate the incident infrared radiation by shell real-timely. And the primary output of detector except the shell radiation can be corrected by the gain coefficient. This method has been tested in real infrared imaging system, reaching high correction level, reducing fixed pattern noise, adapting wide temperature range.
Efficient color correction method for smartphone camera-based health monitoring application.
Duc Dang; Chae Ho Cho; Daeik Kim; Oh Seok Kwon; Jo Woon Chong
Smartphone health monitoring applications are recently highlighted due to the rapid development of hardware and software performance of smartphones. However, color characteristics of images captured by different smartphone models are dissimilar each other and this difference may give non-identical health monitoring results when the smartphone health monitoring applications monitor physiological information using their embedded smartphone cameras. In this paper, we investigate the differences in color properties of the captured images from different smartphone models and apply a color correction method to adjust dissimilar color values obtained from different smartphone cameras. Experimental results show that the color corrected images using the correction method provide much smaller color intensity errors compared to the images without correction. These results can be applied to enhance the consistency of smartphone camera-based health monitoring applications by reducing color intensity errors among the images obtained from different smartphones.
MRI-Based Computed Tomography Metal Artifact Correction Method for Improving Proton Range Calculation Accuracy
International Nuclear Information System (INIS)
Park, Peter C.; Schreibmann, Eduard; Roper, Justin; Elder, Eric; Crocker, Ian; Fox, Tim; Zhu, X. Ronald; Dong, Lei; Dhabaan, Anees
Purpose: Computed tomography (CT) artifacts can severely degrade dose calculation accuracy in proton therapy. Prompted by the recently increased popularity of magnetic resonance imaging (MRI) in the radiation therapy clinic, we developed an MRI-based CT artifact correction method for improving the accuracy of proton range calculations. Methods and Materials: The proposed method replaces corrupted CT data by mapping CT Hounsfield units (HU number) from a nearby artifact-free slice, using a coregistered MRI. MRI and CT volumetric images were registered with use of 3-dimensional (3D) deformable image registration (DIR). The registration was fine-tuned on a slice-by-slice basis by using 2D DIR. Based on the intensity of paired MRI pixel values and HU from an artifact-free slice, we performed a comprehensive analysis to predict the correct HU for the corrupted region. For a proof-of-concept validation, metal artifacts were simulated on a reference data set. Proton range was calculated using reference, artifactual, and corrected images to quantify the reduction in proton range error. The correction method was applied to 4 unique clinical cases. Results: The correction method resulted in substantial artifact reduction, both quantitatively and qualitatively. On respective simulated brain and head and neck CT images, the mean error was reduced from 495 and 370 HU to 108 and 92 HU after correction. Correspondingly, the absolute mean proton range errors of 2.4 cm and 1.7 cm were reduced to less than 2 mm in both cases. Conclusions: Our MRI-based CT artifact correction method can improve CT image quality and proton range calculation accuracy for patients with severe CT artifacts
Investigation of Compton scattering correction methods in cardiac SPECT by Monte Carlo simulations
International Nuclear Information System (INIS)
Silva, A.M. Marques da; Furlan, A.M.; Robilotta, C.C.
The goal of this work was the use of Monte Carlo simulations to investigate the effects of two scattering correction methods: dual energy window (DEW) and dual photopeak window (DPW), in quantitative cardiac SPECT reconstruction. MCAT torso-cardiac phantom, with 99m Tc and non-uniform attenuation map was simulated. Two different photopeak windows were evaluated in DEW method: 15% and 20%. Two 10% wide subwindows centered symmetrically within the photopeak were used in DPW method. Iterative ML-EM reconstruction with modified projector-backprojector for attenuation correction was applied. Results indicated that the choice of the scattering and photopeak windows determines the correction accuracy. For the 15% window, fitted scatter fraction gives better results than k = 0.5. For the 20% window, DPW is the best method, but it requires parameters estimation using Monte Carlo simulations. (author)
Ballistic deficit correction methods for large Ge detectors-high counting rate study
International Nuclear Information System (INIS)
Duchene, G.; Moszynski, M.
This study presents different ballistic correction methods versus input count rate (from 3 to 50 kcounts/s) using four large Ge detectors of about 70 % relative efficiency. It turns out that the Tennelec TC245 linear amplifier in the BDC mode (Hinshaw method) is the best compromise for energy resolution throughout. All correction methods lead to narrow sum-peaks indistinguishable from single Γ lines. The full energy peak throughput is found representative of the pile-up inspection dead time of the corrector circuits. This work also presents a new and simple representation, plotting simultaneously energy resolution and throughput versus input count rate. (TEC). 12 refs., 11 figs
New age- and sex-specific criteria for QT prolongation based on rate correction formulas that minimize bias at the upper normal limits.
Rautaharju, Pentti M; Mason, Jay W; Akiyama, Toshio
Existing formulas for rate-corrected QT (QTc) commonly fail to properly adjust the upper normal limits which are more critical than the mean QTc for evaluation of prolonged QT. Age- and sex-related differences in QTc are also often overlooked. Our goal was to establish criteria for prolonged QTc using formulas that minimize QTc bias at the upper normal limits. Strict criteria were used in selecting a study group of 57,595 persons aged 5 to 89 years (54% women) and to exclude electrocardiograms (ECG) with possible disease-associated changes. Two QT rate adjustment formulas were identified which both minimized rate-dependency in the 98 th percentile limits: QTcmod, based on an electrophysiological model (QTcMod = QTx(120 + HR)/180)), and QTcLogLin, a power function of the RR interval with exponents 0.37 for men and 0.38 for women. QTc shortened in men during adolescence and QTcMod became 13 ms shorter than in women at age 20-29 years. The sex difference was maintained through adulthood although decreasing with age. The criteria established for prolonged QTc were: Age < 40 years, men 430 ms, women 440 ms; Age 40 to 69, men 440 ms, women 450 ms; Age ≥ 70 years, men 455 ms, and women 460 ms. Sex difference in QTc originates from shortened QT in adolescent males. Upper normal limits for QTc vary substantially by age and sex, and it is essential to use age- and sex-specific criteria for evaluation of QT prolongation. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging.
Greve, Douglas N; Salat, David H; Bowen, Spencer L; Izquierdo-Garcia, David; Schultz, Aaron P; Catana, Ciprian; Becker, J Alex; Svarer, Claus; Knudsen, Gitte M; Sperling, Reisa A; Johnson, Keith A
A cross-sectional group study of the effects of aging on brain metabolism as measured with (18)F-FDG-PET was performed using several different partial volume correction (PVC) methods: no correction (NoPVC), Meltzer (MZ), Müller-Gärtner (MG), and the symmetric geometric transfer matrix (SGTM) using 99 subjects aged 65-87years from the Harvard Aging Brain study. Sensitivity to parameter selection was tested for MZ and MG. The various methods and parameter settings resulted in an extremely wide range of conclusions as to the effects of age on metabolism, from almost no changes to virtually all of cortical regions showing a decrease with age. Simulations showed that NoPVC had significant bias that made the age effect on metabolism appear to be much larger and more significant than it is. MZ was found to be the same as NoPVC for liberal brain masks; for conservative brain masks, MZ showed few areas correlated with age. MG and SGTM were found to be similar; however, MG was sensitive to a thresholding parameter that can result in data loss. CSF uptake was surprisingly high at about 15% of that in gray matter. The exclusion of CSF from SGTM and MG models, which is almost universally done, caused a substantial loss in the power to detect age-related changes. This diversity of results reflects the literature on the metabolism of aging and suggests that extreme care should be taken when applying PVC or interpreting results that have been corrected for partial volume effects. Using the SGTM, significant age-related changes of about 7% per decade were found in frontal and cingulate cortices as well as primary visual and insular cortices. Copyright © 2016 Elsevier Inc. All rights reserved.
Different Partial Volume Correction Methods Lead to Different Conclusions: an 18F-FDG PET Study of Aging
Greve, Douglas N.; Salat, David H.; Bowen, Spencer L.; Izquierdo-Garcia, David; Schultz, Aaron P.; Catana, Ciprian; Becker, J. Alex; Svarer, Claus; Knudsen, Gitte; Sperling, Reisa A.; Johnson, Keith A.
A cross-sectional group study of the effects of aging on brain metabolism as measured with 18F-FDG PET was performed using several different partial volume correction (PVC) methods: no correction (NoPVC), Meltzer (MZ), Müller-Gärtner (MG), and the symmetric geometric transfer matrix (SGTM) using 99 subjects aged 65-87 from the Harvard Aging Brain study. Sensitivity to parameter selection was tested for MZ and MG. The various methods and parameter settings resulted in an extremely wide range of conclusions as to the effects of age on metabolism, from almost no changes to virtually all of cortical regions showing a decrease with age. Simulations showed that NoPVC had significant bias that made the age effect on metabolism appear to be much larger and more significant than it is. MZ was found to be the same as NoPVC for liberal brain masks; for conservative brain masks, MZ showed few areas correlated with age. MG and SGTM were found to be similar; however, MG was sensitive to a thresholding parameter that can result in data loss. CSF uptake was surprisingly high at about 15% of that in gray matter. Exclusion of CSF from SGTM and MG models, which is almost universally done, caused a substantial loss in the power to detect age-related changes. This diversity of results reflects the literature on the metabolism of aging and suggests that extreme care should be taken when applying PVC or interpreting results that have been corrected for partial volume effects. Using the SGTM, significant age-related changes of about 7% per decade were found in frontal and cingulate cortices as well as primary visual and insular cortices. PMID:26915497
Novel, cyclic heat dissipation method for the correction of natural temperature gradients in sap flow measurements. Part 1. Theory and application.
Lubczynski, Maciek W; Chavarro-Rincon, Diana; Roy, Jean
Natural temperature gradient (NTG) can be a significant problem in thermal sap flow measurements, particularly in dry environments with sparse vegetation. To resolve this problem, we propose a novel correction method called cyclic heat dissipation (CHD) in its thermal dissipation probe (TDP) application. The CHD method is based on cyclic, switching ON/OFF power schema measurements and a three-exponential model, extrapolating measured signal to steady state thermal equilibrium. The extrapolated signal OFF represents NTG, whereas the extrapolated signal ON represents standard TDP signal, biased by NTG. Therefore, subtraction of the OFF signal from the ON signal allows defining the unbiased TDP signal, finally processed according to standard Granier calibration. The in vivo Kalahari measurements were carried out in three steps on four different tree species, first as NTG, then as standard TDP and finally in CHD mode, each step for ∼1-2 days. Afterwards, each tree was separated from its stem following modified Roberts' (1977) procedure, and CHD verification was applied. The typical NTG varying from ∼0.5 °C during night-time to -1 °C during day-time, after CHD correction, resulted in significant reduction of sap flux densities (J(p)) as compared with the standard TDP, particularly distinct for low J(p). The verification of the CHD method indicated ∼20% agreement with the reference method, largely dependent on the sapwood area estimate. The proposed CHD method offers the following advantages: (i) in contrast to any other NTG correction method, it removes NTG bias from the measured signal by using in situ, extrapolated to thermal equilibrium signal; (ii) it does not need any specific calibration making use of the standard Granier calibration; (iii) it provides a physical background to the proposed NTG correction; (iv) it allows for power savings; (v) it is not tied to TDP, and so can be adapted to other thermal methods. In its current state, the CHD data
Integrals of random fields treated by the model correction factor method
DEFF Research Database (Denmark)
Franchin, P.; Ditlevsen, Ove Dalager; Kiureghian, Armen Der
The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals ...
Model correction factor method for reliability problems involving integrals of non-Gaussian random fields
DEFF Research Database (Denmark)
Franchin, P.; Ditlevsen, Ove Dalager; Kiureghian, Armen Der
The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals ...
A Geometric Correction Method of Plane Image Based on OpenCV
Directory of Open Access Journals (Sweden)
Li Xiaopeng
Full Text Available Using OpenCV, a geometric correction method of plane image from single grid image in a state of unknown camera position is presented. The method can remove the perspective and lens distortions from an image. The method is simple and easy to implement, and the efficiency is high. Experiments indicate that this method has high precision, and can be used in some domains such as plane measurement.
Bias in calculated keff from subcritical measurements by the 252Cf-source-driven noise analysis method
International Nuclear Information System (INIS)
Mihalczo, J.T.; Valentine, T.E.
The development of MCNP-DSP, which allows direct calculation of the measured time and frequency analysis parameters from subcritical measurements using the 252 Cf-source-driven noise analysis method, permits the validation of calculational methods for criticality safety with in-plant subcritical measurements. In addition, a method of obtaining the bias in the calculations, which is essential to the criticality safety specialist, is illustrated using the results of measurements with 17.771-cm-diam, enriched (93.15), unreflected, and unmoderated uranium metal cylinders. For these uranium metal cylinders the bias obtained using MCNP-DSP and ENDF/B-V cross-section data increased with subcriticality. For a critical experiment [height (h) = 12.629 cm], it was -0.0061 ± 0.0003. For a 10.16-cm-high cylinder (k ∼ 0.93), it was 0.0060 ± 0.0016, and for a subcritical cylinder (h = 8.13 cm, k ∼ 0.85), the bias was -0.0137 ± 0.0037, more than a factor of 2 larger in magnitude. This method allows the nuclear criticality safety specialist to establish the bias in calculational methods for criticality safety from in-plant subcritical measurements by the 252 Cf-source-driven noise analysis method
An Investigation on the Efficiency Correction Method of the Turbocharger at Low Speed
Directory of Open Access Journals (Sweden)
Jin Eun Chung
Full Text Available The heat transfer in the turbocharger occurs due to the temperature difference between the exhaust gas and intake air, coolant, and oil. This heat transfer causes the efficiency of the compressor and turbine to be distorted, which is known to be exacerbated during low rotational speeds. Thus, this study proposes a method to mitigate the distortion of the test result data caused by heat transfer in the turbocharger. With this method, the representative compressor temperature is defined and the heat transfer rate of the compressor is calculated by considering the effect of the oil and turbine inlet temperatures at low rotation speeds, when the cold and the hot gas test are simultaneously performed. The correction of compressor efficiency, depending on the turbine inlet temperature, was performed through both