ESTIMATION IN MULTIVARIATE t LINEAR MIXED MODELS FOR MULTIPLE LONGITUDINAL DATA

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51866

Title:	ESTIMATION IN MULTIVARIATE t LINEAR MIXED MODELS FOR MULTIPLE LONGITUDINAL DATA
Authors:	Wang,WL;Fan,TH
Contributors:	統計研究所
Keywords:	MAXIMUM-LIKELIHOOD INFERENCE;BAYESIAN-ANALYSIS;DISTRIBUTIONS;ALGORITHM;ECM;ERRORS;MCMC;EM
Date:	2011
Issue Date:	2012-03-27 19:07:57 (UTC+8)
Publisher:	國立中央大學
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.
Relation:	STATISTICA SINICA
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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