Relatório de pesquisa 26/08
Robust Linear Mixed Models with Skew-Normal Independent Distributions from a Bayesian Perspective, Victor H. Lachos, Dipak K. Dey, and Vicente G. Cancho, submitted Nov. 28.
Abstract
Linear mixed models were developed to handle clustered
data and have been a topic of increasing interest in statistics for the
past fifty 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, the skew-t distribution, the skew-slash distribution and
the skew contaminated normal distribution 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.
Mathematics Subject Classifications
(2000):
Keywords: Gibbs Algorithms; Linear mixed models; MCMC; Metropolis-Hastings; Skew-normal/independent distribution
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November 28, 2008
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