|
Activity Number:
|
103
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 7, 2006 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #307316 |
|
Title:
|
Variable Selection in Linear Mixed Models for Longitudinal Data
|
|
Author(s):
|
Lan Lan*+ and Daowen Zhang and Hao Zhang
|
|
Companies:
|
North Carolina State University and sanofi-aventis and North Carolina State University
|
|
Address:
|
4806 Rockport Drive, Durham, NC, 27703,
|
|
Keywords:
|
oracle property ; REML ; SCAD ; variance components
|
|
Abstract:
|
Fan and Li (JASA 2001) proposed a family of variable selection procedures for certain parametric models via a nonconcave penalized likelihood approach, where significant variable selection and parameter estimation were done simultaneously, and the procedures were shown to have the oracle property. In this presentation, we extend the nonconcave penalized likelihood approach to linear mixed models for longitudinal data. Two new approaches are proposed to select significant covariates and estimate fixed effect parameters and variance components. In particular, we show the new approaches also possess the oracle property when the tuning parameter is chosen appropriately. We assess the performance of the proposed approaches via simulation and apply the procedures to data from the Multicenter AIDS Cohort Study.
|
