Mathematical Sciences: Statistical Inference Under PatternedAlternatives Based on the Principle of Isotonic Regression
{'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 deals with statistical inference concerning                  hypotheses in which a vector of parameters is contained in a                    union of cones defined by order restrictions.  Such constraints                 arise naturally when the parameters are known to follow a                       pattern.  The aim of the research is to extend the theory of                    isotonic regression, which is used in order restricted inference,               to this more general setting.  Three specific approaches are                    proposed.  The first involves modifying algorithms used for                     calculating the isotonic regression of a vector.  The second is                 to investigate the distributional properties of test statistics                 based on contrasts, including a simultaneous multiple comparison                procedure.                                                                           This research in the theory of statistical inference is a                  major effort in a current thrust to create statistics that are                  valid when the variable of interest is ordered.  The current                    state of the art in statistical inference assumes the variable to               be categorial or continuous.  This research aims to extend                      statistics to a large class of variables for which there is not a               rigorous theory.
[{'FirstName': 'Robert', 'LastName': 'Kelly', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Robert Kelly', 'EmailAddress': None, 'NSF_ID': '000316180', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Thomas', 'LastName': 'Hettmansperger', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Thomas P Hettmansperger', 'EmailAddress': 'tph@stat.psu.edu', 'NSF_ID': '000294495', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]
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{'Code': '1269', 'Text': 'STATISTICS'}
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