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

Abstract Details

Activity Number: 170
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309282
Title: Regularized REML for Estimation and Selection of Fixed and Random Effects in Linear Mixed-Effects Models
Author(s): Sijian Wang*+ and Peter Song and Ji Zhu
Companies: University of Wisconsin-Madison and University of Michigan and University of Michigan
Address: , , 53706,
Keywords: Oracle property ; REML ; Regularization ; Penalization ; Variable Selection
Abstract:

The linear mixed effects model is widely used in the analysis of clustered or longitudinal data. In the practice of LMM, inference on the structure of both fixed and random effects components is of great importance not only to yield proper interpretation of subject-specific effects but also to draw valid statistical conclusions. In this paper, we propose a novel method of regularized restricted maximum likelihood to select fixed and random effects simultaneously in the LMM. The Cholesky decomposition is invoked to ensure the positive-definiteness of the selected covariance matrix of random effects, and selected random effects are invariant with respect to the ordering of predictors appearing in the model. We develop a new algorithm that solves the related optimization problem effectively. Both simulation studies and data analysis are included for illustration.


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