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

Abstract Details

Activity Number: 523
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #306354
Title: Multivariate t Linear Mixed Models with AR(p) Errors for Multiple Longitudinal Data
Author(s): Wan-Lun Wang*+ and Tsai-Hung Fan
Companies: National Central University, Taiwan and National Central University, Taiwan
Address: Graduate Institute of Statistics, Jhongli City, Taoyuan County, 32049, Taiwan
Keywords: ECME algorithm ; MLMM ; MtLMM ; Outliers ; Random effects ; Score test

The multivariate linear mixed model (MLMM) has been a widely used tool for a joint analysis of more than one series of longitudinal data. Motivated by a concern of sensitivity to potential outliers and data with longer-than-normal tails or possible serial correlation, we aim to develop a robust generalization of the MLMM, which is constructed by using the multivariate t distribution on the random effects and the within-subject errors with AR(p)dependence structure. A score test for inspection of the existence of autocorrelation among within-subject errors is derived. An efficient ECME procedure is developed for computing the maximum likelihood estimates. We demonstrate the proposed methodology on a dataset of the ACTG 175 study.

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