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ESTIMATION IN MULTIVARIATE t LINEAR MIXED MODELS FOR MULTIPLE LONGITUDINAL DATA

Wan-Lun Wang and Tsai-Hung Fan
Statistica Sinica
Vol. 21, No. 4 (October 2011), pp. 1857-1880
Stable URL: http://www.jstor.org/stable/24309658
Page Count: 24
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
ESTIMATION IN MULTIVARIATE t LINEAR MIXED MODELS FOR MULTIPLE LONGITUDINAL DATA
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Abstract

The multivariate linear mixed model (MLMM) is a frequently used tool for a joint analysis of more than one series of longitudinal data. Motivated by a concern of sensitivity to potential outliers or data with longer-than-normal tails and possible serial correlation, we develop a robust generalization of the MLMM that is constructed by using the multivariate t distribution and a parsimonious AR(p) dependence structure for the within-subject errors. A score test for the inspection of autocorrelation among within-subject errors is derived. A hybrid ECME-scoring procedure is developed for computing the maximum likelihood estimates with standard errors as a by-product. The methodology is illustrated through an application to a set of AIDS data and several simulation studies.

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