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|Title:||Bayesian inference for skew-normal linear mixed models|
|Author:||Arellano-Valle, R. B.|
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
Distribuição normal assimétrica
Métodos MCMC (Estatística)
|Editor:||Taylor & Francis|
|Citation:||Journal Of Applied Statistics. Routledge Journals, Taylor & Francis Ltd, v. 34, n. 6, n. 663, n. 682, 2007.|
|Appears in Collections:||IMECC - Artigos e Outros Documentos|
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