The traditional methods of estimating the variance components in random and mixed effects models, namely the ANOVA, ML and the REML have their limitations. This article reviews estimation of variance components using a decision theoretic and Bayesian point of view. The question of choice of priors is also addressed using a criterion of matching asymptotically the posterior probability of a credible interval and the corresponding frequentist coverage probability.