Choice of noninformative priors for the variance components of an unbalanced one-way random model

Abstract

Several noninformative priors for the variance components of an unbalanced one-way model are proposed. A simulation study is performed to compare the posterior means of the variance components based on the different priors. These estimators are also compared to the restricted maximum likelihood estimator (REML). Under the squared error loss, this simulation study indicates that the invariant priors lead to the optimal estimators of the variance components.