Mathematical Sciences: Statistical Theory and Methodology:  Regression Models, Data Transformation, Subsampling and     Likelihoods
{'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 investigate (i) resampling methods for                 robust assessments of the precision and significance of                         statistical estimates, (ii) the construction of reference sets                  for statistical inference from experimental and observational                   studies, (iii) methods for choosing and using scale                             transformations to enlarge the scope of standard statistical                    methods, (iv) generalized regression models, (v) likelihood                     theory and methods, (vi) mixtures of distributions, (vii)                       comparative tests with truncated data.                                               This research is in the field of Statistics.  The new                      methods proposed have become feasible with affordable computing.                Improvement over existing statistical methods is foreseen,                      specifically when data is sparse and when they are not obtained                 under in controlled experiments.
{'FirstName': 'David', 'LastName': 'Hinkley', 'PI_MID_INIT': 'V', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David V Hinkley', 'EmailAddress': None, 'NSF_ID': '000448083', 'StartDate': '06/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '126900', 'Text': 'STATISTICS'}, {'Code': '127100', 'Text': 'COMPUTATIONAL MATHEMATICS'}]
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