Mathematical Sciences: Time Series Analysis and LongitudinalData
{'Code': '03040107', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman', 'PO_EMAI': None, 'PO_PHON': None}
This research is to develop an approach to modelling                    correlation over time by means of auto-regressive moving average                estimates rather than by means of the traditional assumption of                 equicorrelated repeated measures.  Traditional statistical theory               is restricted by the assumptions that (1) measurements taken on                 experimental subjects are all taken serially at the same times,                 (2) the series of measurements taken on each subject can be well                represented by polynomials of the same degree, (3) the polynomial               coefficients have a multivariate normal distribution across                     subjects and (4) the number of subjects is greater than the sum                 of the degree of the polynomials and the number of constants used               to model the dependent variables in the multivariate analysis.                  The proposed research aims to provide a theoretical framework                   that will accommodate experiments where these assumptions are                   unrealistic and invalid.  A major tool to be used in determining                algorithms for calculating the exact likelihoods is the Kalman                  filter, by which serial correlations can be modeled within each                 subject.                                                                                This research is in the general area of time series                     analysis, an area receiving an increasing amount of attention in                statistics.  The attention is reflective of the push to free                    statistical methods from "classical" assumptions which were                     largely imposed because of the traditionally high costs of                      computation.  The aim of this research is to provide sound                      statistical methods for a common experimental situation;                        experiments in this class do not have appropriate, i.e.                         meaningful, statistical tools available for assessing effects and               trends.  The experimental situation which will be impacted by                   this research occurs in many sciences and is characterized by the               goal of assessing the effects of some factors or treatments that                may change over time, coupled with a lack of experimenter control               over exactly when and where measurements can be taken.
{'FirstName': 'Richard', 'LastName': 'Jones', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Richard H Jones', 'EmailAddress': None, 'NSF_ID': '000215034', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Colorado at Denver', 'CityName': 'AURORA', 'ZipCode': '800452571', 'PhoneNumber': '3037240090', 'StreetAddress': '13001 E 17TH PL STE F428', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Colorado', 'StateCode': 'CO', 'CONGRESSDISTRICT': '06', 'CONGRESS_DISTRICT_ORG': 'CO06', 'ORG_UEI_NUM': 'MW8JHK6ZYEX8', 'ORG_LGL_BUS_NAME': 'THE REGENTS OF THE UNIVERSITY OF COLORADO', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '126900', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8702195.xml'}