ESTIMATION OF VARIANCE AND COVARIANCE COMPONENTS IN ELLIPTICALLY CONTOURED DISTRIBUTIONS

Abstract

This paper considers the problems of estimating the univariate and multivariate components of variance in elliptically contoured distribution(ECD)models in a decision-theoretic setup. Empirical Bayes or generalized Bayes estimators and several other positive or non-negative(definite)estimators, superior to usual ANOVA(unbiased)estimators of the variance components, are obtained. The robustness of the dominance results is investigated, and it is shown that all dominance results under normal models remain true within a specific class of distributions.