Statistical Modelling 8 (2008), 221241
Skew random effects in multilevel binomial models: an alternative to
nonparametric approach
Jungfeng Liu
Biometrics Division, The Cancer Institute of New Jersey,
195 Little Albany Street,
New Brunswick, NJ 08901
and
Department of Biostatistics, School of Public Health,
University of Medicine and Dentistry of New Jersey,
Piscataway, NJ 08854
U.S.A.
eMail:
liu16@umdnj.edu
Dipak K. Dey
Department of Statistics,
University of Connecticut,
Storrs
U.S.A.
Abstract:
Compared to modelling observable data, it is more difficult to choose a
suitable distribution to describe latent variables since no prior knowledge
or observable information can be used and only normal or nonparametric
distributions are mainly applied to random effects for generalized linear
mixed models (GLMMs) in the literature. To enhance the modelling toolkit,
this article investigates a class of parametric skew elliptical random
effects in multilevel binomial regression models using a Bayesian approach;
the class includes skew normal, skew Students’ t-distributions and others.
Skewness mechanism is considered through multiplying skewness parameter
Δ by standardized folded elliptical random variables, and the posterior
sampling is realized by working on a binary skewness indicator (BSI)
instead of continuous Δ for parameter identifiability. Simulation
study shows that the original continuous skewness parameter Δ and the
posterior mean of BSI may have dichotomous signs to describe the directional
(right/left) skewness; thus we address the importance of assuming specific
random effects distribution and interpreting the skewness carefully. The
methodology is exemplified through reanalyzing a teratogenic activity
study of two niacin analogs published in the biological literature, and
sampling-based model comparison shows that the parametric skew normal
random effects model works largely better than nonparametric Dirichlet
process mixture models for this data set.
Keywords:
Binomial; binary skewness indicator (BSI);
generalized linear mixed model (GLMM); multilevel; random effects;
sign switching; skewness
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