A flexible class of parametric distributions for Bayesian linear mixed models

• Mohsen Maleki ^[1] ; Darren Wraith ^[2] ; Reinaldo B. Arellano-Valle ^[3]
  1. [1] Shiraz University

     Shiraz University

     Irán

     
  2. [2] Queensland University of Technology

     Queensland University of Technology

     Australia

     
  3. [3] Pontificia Universidad Católica de Chile

     Pontificia Universidad Católica de Chile

     Santiago, Chile

     

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 2, 2019, págs. 543-564
• Idioma: inglés
• DOI: 10.1007/s11749-018-0590-6
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this paper, we consider a linear mixed effect model (LMM) assuming that the random effect and error terms follow an unrestricted skew-normal generalized-hyperbolic (SUNGH) distribution. The SUNGH is a broad class of flexible distributions that includes various other well-known asymmetric and symmetric families and provides a high degree of flexibility for the modeling of complex multivariate data with different directions and degrees of asymmetry, kurtosis and heavy tails. The choice of the best fitting distribution can proceed quite naturally through parameter estimation or by placing constraints on specific parameters and assessing using model choice criteria. We estimate parameters of the LMM using a Bayesian approach and examine the performance of the proposed methodology on simulated and real data from a clinical trial on treatment options for schizophrenia (Lapierre et al. Acta Psychiatric Scandinavica 82:72–76, 1990; Ho and Lin Biom J 52(4):449–469, 2010).