This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 551
Type: Invited
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #306133
Title: Semiparametric Marginal Mean Models for Longitudinal Survey Data
Author(s): Li Wang* and Suojin Wang+
Companies: The University of Georgia and Texas A&M University
Address: Department of Statistics, College Station, TX, 77843, USA
Keywords: Generalized estimating equations ; Jackknife ; Partially linear models ; Spline ; Superpopulation ; Variable selection

A class of semiparametric marginal models, containing both linear and nonlinear additive components, are investigated for longitudinal surveys. We propose an extension of the parametric design-weighted generalized estimating equations (GEE) approach of Rao (1998) based on a spline approximation of the nonparametric components. We apply the EF-Jackknife to estimate the variance for the case of complex survey data. We also develop a variable selection procedure to identify significant linear components using the smoothly clipped absolute deviation penalty (SCAD). A fast and efficient estimation algorithm is developed for the users to analyze complex longitudinal survey data within seconds. The results of Monte Carlo experiments confirm a good behavior of the proposed estimators with samples of moderate size. A survey example is used to illustrate the application of the proposed method.

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