This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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551
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Type:
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Invited
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #306133 |
Title:
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Semiparametric Marginal Mean Models for Longitudinal Survey Data
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Author(s):
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Li Wang* and Suojin Wang+
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Companies:
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The University of Georgia and Texas A&M University
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Address:
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Department of Statistics, College Station, TX, 77843, USA
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Keywords:
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Generalized estimating equations ;
Jackknife ;
Partially linear models ;
Spline ;
Superpopulation ;
Variable selection
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Abstract:
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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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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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