Mathematical Sciences: Asymptotic Methods of Statistics
{'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 will deal with asymptotic problems in                         nonparametric inference.  For inference on regression, due to the               possibility of heavy-tailed residual distribution, regression is                viewed in more general way than conditional expectation.                        Regression estimation with homogeneous residuals and various                    generalizations of the well-known Theil estimator will be sought.               Inference on autoregressive processes with heavy-tailed residuals               is part of such problems as well.  Kernel and nearest neighbor                  methods will be used for estimation of a conditional quantile                   function.  The important issue in these methods is the choice of                the smoothing parameter.  Other topics of study will include                    inference on linear regression based on truncated and censored                  data,  use of covariate information via nonparametric regression                in observational studies, methods for parallel regression, non-                 parallel regression and tests of parallelity.
{'FirstName': 'P.', 'LastName': 'Bhattacharya', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'P. K Bhattacharya', 'EmailAddress': None, 'NSF_ID': '000249965', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-Davis', 'CityName': 'DAVIS', 'ZipCode': '956186153', 'PhoneNumber': '5307547700', 'StreetAddress': '1850 RESEARCH PARK DR STE 300', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'CA04', 'ORG_UEI_NUM': 'TX2DAGQPENZ5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, DAVIS', 'ORG_PRNT_UEI_NUM': 'NUDGYLBB4S99'}
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{'Code': '1269', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8703401.xml'}