Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/201785
Type: Artigo de periódico
Title: Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.
Author: Garay, Aldo M
Castro, Luis M
Leskow, Jacek
Lachos, Victor H
Abstract: In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.
Subject: Hiv Viral Load
Censored Data
Expectation Conditional Maximization Algorithm
Longitudinal Data
Outliers
Rights: fechado
Identifier DOI: 10.1177/0962280214551191
Address: http://www.ncbi.nlm.nih.gov/pubmed/25296865
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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