Semiparametric Marginal Mean Models for Longitudinal Survey Data

• Activity Number: 551
• Type: Invited
• Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Li Wang* and Suojin Wang+
• Companies: The University of Georgia and Texas A&M University
• Keywords: Generalized estimating equations ; Jackknife ; Partially linear models ; Spline ; Superpopulation ; Variable selection

Abstract

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.