Testing random effects in the linear mixed model using approximate bayes factors.

ABSTRACT:

Summary

Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the varian

SUBMITTER: Saville BR 

PROVIDER: S-EPMC3136354 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature