Zhen Chen, Ph.D.
Research Fellow
Biostatistics Branch
National Institute of Environmental Health Sciences
National Institutes of Health
Random Effects Selection in Linear Mixed Models
In collaboration with Dr. David B. Dunson
We address the important
practical problem of how to select the random effects component in a linear
mixed model. A hierarchical Bayesian model is used to identify any
random effect with 0 variance.
The proposed approach reparameterizes the
mixed model so that functions of the covariance parameters of the random
effects distribution are incorporated as regression coefficients on standard
normal latent variables. We allow random effects to effectively drop out of the
model by choosing mixture priors with point mass at zero for the random effects
variances. Due to the reparameterization,
the model enjoys a conditionally linear structure that facilitates the use
of normal conjugate priors. We demonstrate that posterior
computation can proceed via a simple and efficient Markov chain