A Bayesian approach to the estimation of variance components for the unbalanced one-way random model

The estimation of variance components in the one-way random model with unequal sample sizes is studied. A simulation study that indicates that modes of posterior distributions have good sampling properties compared with other estimators is presented. The posterior distributions are calculated using a noninformative prior distribution that is uniform on the intraclass correlation. A simulation study for the estimation of the ratio of the variance components is also presented, together with a study of the sampling properties of highest posterior density regions for this ratio, Bayesian estimators appear to be viable competitors to the many classical alternatives in a sampling framework.