A flexible class of parametric distributions for Bayesian linear mixed models

Mohsen Maleki, Darren Wraith () and Reinaldo B. Arellano-Valle
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Mohsen Maleki: Shiraz University
Darren Wraith: Queensland University of Technology (QUT)
Reinaldo B. Arellano-Valle: Universidad Católica de Chile

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 2, No 19, 543-564

Abstract: Abstract 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).

Keywords: Bayesian analysis; Linear mixed effect model; MCMC method; Unrestricted skew-normal generalized-hyperbolic distribution; Unrestricted skew-normal distribution; 62J02; 62P10; 62F15 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11749-018-0590-6

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