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