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

Abstract Details

Activity Number: 534
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #309216
Title: Model Selection for Nonnested Linear Mixed Models
Author(s): Che Smith* and Lloyd Edwards+
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: 3101 McGavran-Greenberg Hall, Chapel Hill, NC, 27599-7420,
Keywords: model selection ; information criterion ; bootstrap ; linear mixed models

In research of linear mixed models, selection between nonnested models (differing in both mean and covariance structure) has been an area that remains unaddressed. In previous research, the Extended Information Criterion (EIC) has been shown to perform as well, or better, than the Akaike Information Criterion (AIC) for comparing nonnested linear mixed models using small-sample data. We assess the performance of the EIC using data from a large-sample longitudinal study of elderly subjects - the North Carolina Established Populations for the Epidemiological Study of the Elderly (NC EPESE).

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