Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/55351
Type: Artigo
Title: Bayesian inference for skew-normal linear mixed models
Author: Arellano-Valle, R. B.
Bolfarine, H.
Lachos, V. H.
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.
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 bot
Subject: Inferencia bayesiana
Distribuição normal assimétrica
Métodos MCMC (Estatística)
Country: Reino Unido
Editor: Taylor & Francis
Citation: Journal Of Applied Statistics. Routledge Journals, Taylor & Francis Ltd, v. 34, n. 6, n. 663, n. 682, 2007.
Rights: fechado
Identifier DOI: 10.1080/02664760701236905
Address: https://www.tandfonline.com/doi/full/10.1080/02664760701236905
Date Issue: 2007
Appears in Collections:IMECC - Artigos e Outros Documentos

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