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Type: Artigo de periódico
Title: Bayesian inference for skew-normal linear mixed models
Author: Arellano-Valle, RB
Bolfarine, H
Lachos, VH
Abstract: Linear mixed models (LMM) are frequently used to analyze repeated measures data, because they are more flexible to modelling the correlation within-subject, often present in this type of data. The most popular LMM for continuous responses assumes that both the random effects and the within-subjects errors are normally distributed, which can be an unrealistic assumption, obscuring important features of the variations present within and among the units ( or groups). This work presents skew-normal liner mixed models (SNLMM) that relax the normality assumption by using a multivariate skew-normal distribution, which includes the normal ones as a special case and provides robust estimation in mixed models. The MCMC scheme is derived and the results of a simulation study are provided demonstrating that standard information criteria may be used to detect departures from normality. The procedures are illustrated using a real data set from a cholesterol study.
Subject: Bayesian inference
Gibbs sampler
multivariate skew-normal distribution
Country: Inglaterra
Editor: Routledge Journals, Taylor & Francis Ltd
Rights: fechado
Identifier DOI: 10.1080/02664760701236905
Date Issue: 2007
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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