Semi-parametric Bayesian estimation of mixed-effects models using the multivariate skew-normal distribution

Reyhaneh Rikhtehgaran () and Iraj Kazemi ()

Computational Statistics, 2013, vol. 28, issue 5, 2007-2027

Abstract: In this paper, we develop a semi-parametric Bayesian estimation approach through the Dirichlet process (DP) mixture in fitting linear mixed models. The random-effects distribution is specified by introducing a multivariate skew-normal distribution as base for the Dirichlet process. The proposed approach efficiently deals with modeling issues in a wide range of non-normally distributed random effects. We adopt Gibbs sampling techniques to achieve the parameter estimates. A small simulation study is conducted to show that the proposed DP prior is better at the prediction of random effects. Two real data sets are analyzed and tested by several hypothetical models to illustrate the usefulness of the proposed approach. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Dirichlet process mixture; Gibbs sampling; Hierarchical models; Identifibiliaty problem; Random effects (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:5:p:2007-2027

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DOI: 10.1007/s00180-012-0392-3

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