Abstract #300161

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JSM 2003 Abstract #300161
Activity Number: 230
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #300161
Title: Semiparametric Modeling for Longitudinal Data
Author(s): Jianqing Fan*+ and Runze Li
Companies: Chinese University of Hong Kong and Pennsylvania State University
Address: Dept. of Statistics, Hong Kong, , , Peoples Republic of China
Keywords: profile least-squares ; penalized least-squares ; oracle properties ; bandwidth selection ; model selection
Abstract:

Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences, variable selections and nonparametric goodness-of-fit that frequently arise from longitudinal data analysis. In this talk, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting estimators is established. An innovative class of variable selection procedures is proposed to select significant variables in the semiparametric models. The proposed procedures are distinguished from others in that they simultaneously select significant variables and estimate unknown parameters. With a proper choice of regularization parameters and penalty functions, the proposed variable selection procedures are shown to perform as well as an oracle estimator. A robust standard error formula is derived using a sandwich formula, and empirically tested.

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