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Type: Artigo
Title: Robust linear mixed models with skew-normal independent distributions from a bayesian perspective
Author: Lachos, Victor H.
Dey, Dipak K.
Cancho, Vicente G.
Abstract: Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally, the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol study
Subject: Distribuição normal assimétrica
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.jspi.2009.05.040
Date Issue: 2009
Appears in Collections:IMECC - Artigos e Outros Documentos

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