Mathematical Sciences: Some Topics in Generalized RegressionModels
{'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}
The research which is proposed here is mainly concerned with            two problems in methods associated with generalized linear                      models:  diagnostics for tests of significance and data analytic                methods in the presence of overdispersion.  While there has been                substantial development of diagnostic techniques associated with                point estimation in generalized linear models, the research                     proposed is to study the effects of individual data points on                   tests of hypotheses concerning parameters of the model.  The                    purpose  includes ascertainment of whether the significance of a                test is overly dependent on a very small part of the data, and                  other diagnostic issues.  The proposed technique is based on the                contributions of individual data points to the likelihood ratio                 test statistic.  The second part of the proposed research                       concerns regression and analysis where the response variables are               like binomial or Poisson but exhibit more variation that would be               expected from these probability distributions.  Several issues                  related to this case are proposed for study:  (i)  the                          similarities and differences in overdispersion models based on                  covariates being omitted and based on covariables being measured                with error, (ii) the analysis of data when there is                             overdispersion due to both omitted variables and measurement                    errors, and (iii) data analytic methods involbing interactions                  when there is overdispersion present.                                                   Regression methods are used to predict what some entity will            be or will do based on related information and to determine the                 strength of association between measurements and responses.  The                proposed research will enhance and augment the collection of                    statistical research tools and procedures that are referred to                  collectively as "regression analysis".  This research promises to               lead to improvements in the performance of these tools when                     variables are measured with error and when influencial predictor                variables are unknown or unavailable, and therefore omitted.                    Since this situation is extremely common in experimental and                    social science, improvements will have a wide positive impact.
[{'FirstName': 'Donald', 'LastName': 'Pierce', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Donald A Pierce', 'EmailAddress': None, 'NSF_ID': '000364550', 'StartDate': '06/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Daniel', 'LastName': 'Schafer', 'PI_MID_INIT': 'W', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daniel W Schafer', 'EmailAddress': 'schafer@science.oregonstate.edu', 'NSF_ID': '000479822', 'StartDate': '06/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
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{'Code': '1269', 'Text': 'STATISTICS'}
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