Mathematical Sciences: Likelihood Type Methods in Parametricand Nonparametric Models
{'Code': '03040300', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Sallie Keller-McNulty', 'PO_EMAI': None, 'PO_PHON': None}
This research has as its basic theme the development of                    likelihood and likelihood-based methods for statistical models in               which conventional likelihood methods are poorly understood or                  are known to fail.  The topics fall into several main categories.               These are semiparametric density estimation, mixture estimation                 and testing, composite likelhood, nuisance parameters, and                      algorithms.                                                                          This reseach in the general area of statistics.  It aims to                extend the scope of scientific problems that can rigorously                     analyzed.
{'FirstName': 'Bruce', 'LastName': 'Lindsay', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Bruce G Lindsay', 'EmailAddress': 'bgl@psu.edu', 'NSF_ID': '000437260', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '1269', 'Text': 'STATISTICS'}
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