Mathematical Sciences: Regression Quantile Methods and      Asymptotic Statistical Theory
{'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 objectives involve extending the regression                   quantile methodology and developing new asymptotic approaches to                cases of large parametric dimension.  Methodological aims include               the following:  (a)  to develop adaptive estimators in the linear               regression model which are fully efficient under any assumption                 on the error distribution,  (b)  to define and investigate                      outlier detection prodedures and to apply them for providing                    resistance to the presence of outliers and influential                          observations, (c) to investigate the performance of the                         regression quantile algorithm, and (d) to obtain robust analyses                for nonlinear regression models and for structural models in                    econometrics.  Aims for new asymptotic approaches include the                   following:  (a) to obtain appropriate asymptotic results for the                regression quantile methodology in linear models, (b)  to study                 standard and robust methods for analyzing structural models, and                (c) to investigate the adequancy of such asymptotic                             approximations for real and simulated data sets.                                     This research in the field of statistics is to develop and                 analyze robust econometric procedures.  Robust statistical                      methods are procedures that work well even when applied to                      situations where the underlying assumptions in the mathematics                  are unrealistic and do not hold.  Since mathematical models of                  the economy are approximations of very complex systems for which                the true underlying mathematical representation may well be                     unknown, and since data brings measurement error and other                      uncertainty with its very creation, it is advantageous to have                  statistical techniques that are known to perform well under                     adverse conditions.
{'FirstName': 'Stephen', 'LastName': 'Portnoy', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Stephen Portnoy', 'EmailAddress': 'portnoy@stat.uiuc.edu', 'PI_PHON': '2173336217', 'NSF_ID': '000459703', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1269', 'Text': 'STATISTICS'}, {'Code': '1320', 'Text': 'Economics'}]
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