JSM 2004 - Toronto

Abstract #301642

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Activity Number: 224
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301642
Title: Selecting the Best Linear Mixed Model: The State of the Art
Author(s): Lloyd J. Edwards*+ and Keith E. Muller and Paul W. Stewart
Companies: University of North Carolina, Chapel Hill and University of North Carolina, Chapel Hill and University of North Carolina, Chapel Hill
Address: Dept.of Biostatistics, Chapel Hill, NC, 27599-7420,
Keywords: longitudinal data ; model selection ; repeated measures
Abstract:

The linear mixed model for the analysis of repeated measures can be viewed as the multivariate extension of the univariate linear model. However, unlike standard univariate regression, there is comparatively little literature on model selection for the linear mixed model. We review the state of the art in model selection techniques for comparing parametric linear mixed models. We identify areas for which there are no existing model selection methods and areas where either an existing method has been proven to have undesirable properties or there is a lack of evidence supporting its adoption. The overall conclusion made from this review is that there is much left to do to improve the availability of model selection techniques for linear mixed models.

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