ESTIMATING A COVARIANCE MATRIX OF A NORMAL DISTRIBUTION WITH UNKNOWN MEAN
1993 Volume 23 Issue 2 Pages 131-144
Details
1993 Volume 23 Issue 2 Pages 131-144
For the covariance matrix of the multivariate normal distribution with an unknown mean vector, discontinuous or continuous Stein type truncated estimators have been proposed. This article summarizes a series of recent results and obtains an improved and generalized Bayes estimator based on the Brown-Brewster-Zidek method, well known in the univariate case. The asymptotic risk expansions of the estimators are derived, numerically investigated, and it is revealed that the risk-reductions of the generalized Bayes estimator and an empirical Bayes estimator are considerably great in the large dimensional case.
