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

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  • Reyhaneh Rikhtehgaran
  • Iraj Kazemi

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

Suggested Citation

  • Reyhaneh Rikhtehgaran & Iraj Kazemi, 2013. "Semi-parametric Bayesian estimation of mixed-effects models using the multivariate skew-normal distribution," Computational Statistics, Springer, vol. 28(5), pages 2007-2027, October.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:5:p:2007-2027
    DOI: 10.1007/s00180-012-0392-3
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    References listed on IDEAS

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    6. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, October.
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