JSM 2003 Online Program

Abstract #302159

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Activity Number:	207
Type:	Contributed
Date/Time:	Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor:	IMS
Abstract - #302159
Title:	Efficient Estimation of Semiparametric Generalized Linear Models
Author(s):	Hanxiang Peng*+
Companies:	University of Mississippi
Address:	Hume Hall 305, University, MS, MS 38677,
Keywords:	generalized linear models ; least dispersed regular estimator ; efficient influence function ; kernel estimator ; information lower bound
Abstract:	We calculate the efficient influence function and the information bound of semiparametric generalized linear models and propose efficient estimators of the parameters. The construction of estimators employs the method of scoring, ameliorating primary estimators to attain the efficiency. All the observations are used to estimate the unknown influence functions and to evaluate the estimated, avoiding the technique of "sample splitting" (Bickel, 1982). The models lack the independence structure and hence the conditioning argument used by Schick (1993, 1994, 2002) cannot be applied to circumvent.

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