HIERARCHICAL BAYES ESTIMATION OF NORMAL VARIANCES WITH APPLICATION TO A RANDOM EFFECT MODEL

The problem of simultaneously estimating p normal variances is investigated when the parameters are believed a priori to be similar in size. A hierarchical Bayes approach is employed and the resulting estimator is compared to common estimators used including one proposed by Box and Tiao (1973) using a Bayesian approach with a noninformative prior. The technique is then applied to estimate components of variance in the one way layout random effect model of the analysis of variance. © 1990, Taylor & Francis Group, LLC. All rights reserved.