Extending Multivariate-t Linear Mixed Models For Multiple Longitudinal Data With Censored Responses And Heavy Tails.

dc.creatorWang, Wan-Lun
dc.creatorLin, Tsung-I
dc.creatorLachos, Victor H
dc.date2015-Dec
dc.date2016-05-23T19:43:51Z
dc.date2016-05-23T19:43:51Z
dc.date.accessioned2018-03-29T01:30:58Z
dc.date.available2018-03-29T01:30:58Z
dc.identifierStatistical Methods In Medical Research. , 2015-Dec.
dc.identifier1477-0334
dc.identifier10.1177/0962280215620229
dc.identifierhttp://smm.sagepub.com/content/early/2015/12/11/0962280215620229.abstract
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/236074
dc.identifier26668091
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1304317
dc.descriptionThe analysis of complex longitudinal data is challenging due to several inherent features: (i) more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time; (ii) censorship due to limits of quantification of responses arises left- and/or right- censoring effects; (iii) outliers or heavy-tailed noises are possibly embodied within multiple response variables. This article formulates the multivariate-t linear mixed model with censored responses (MtLMMC), which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient expectation conditional maximization either (ECME) algorithm is developed to carry out maximum likelihood estimation of model parameters. The implementation of the E-step relies on the mean and covariance matrix of truncated multivariate-t distributions. To enhance the computational efficiency, two auxiliary permutation matrices are incorporated into the procedure to determine the observed and censored parts of each subject. The proposed methodology is demonstrated via a simulation study and a real application on HIV/AIDS data.
dc.description
dc.description
dc.languageeng
dc.relationStatistical Methods In Medical Research
dc.relationStat Methods Med Res
dc.rightsembargo
dc.sourcePubMed
dc.subjectAids Clinical Trials
dc.subjectEcme Algorithm
dc.subjectCensored Data
dc.subjectMultivariate Longitudinal Data
dc.subjectOutliers
dc.titleExtending Multivariate-t Linear Mixed Models For Multiple Longitudinal Data With Censored Responses And Heavy Tails.
dc.typeArtículos de revistas


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