Bayesian Inference for Skew-normal Linear Mixed Models

Reinaldo B. Arellano‐Valle, Heleno Bolfarine, Víctor H. Lachos

Type: Article

Publication Date: 2007-08-01

Citations: 120

DOI: https://doi.org/10.1080/02664760701236905

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.

Locations

Journal of Applied Statistics - View - PDF

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