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|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|
|Appears in Collections:||IMECC - Artigos e Outros Documentos|
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