Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics.

ABSTRACT: Generalized linear mixed models are a common statistical tool for the analysis of clustered or longitudinal data where correlation is accounted for through cluster-specific random effects. In practice, the distribution of the random effects is typically taken to be a Normal distribution, although if this does not hold then the model is misspecified and standard estimation/inference may be invalid. An alternative is to perform a so-called nonparametric Bayesian analyses in which one assigns a Dirichlet process (DP) prior to the unknown distribution of the random effects. In this paper we examine operating characteristics for estimation of fixed effects and random effects based on such an analysis under a range of "true" random effects distributions. As part of this we investigate various ap

SUBMITTER: Antonelli J 

PROVIDER: S-EPMC5624537 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature