Consider the problem of estimating a normal mean vector when i.i.d observations are available from a p-dimensional normal distribution with an unknown mean vector and an unknown diagonal dispersion matrix proportional to the identity matrix. By using the improved variance estimation techniques we propose wide classes of shrinkage mean estimators which are uniformly better than the James-Stein estimator. Some of our improved mean estimators are completely new and are not covered by Kubokawa's (1994; A Unified Approach to Improving Equivariant Estimators. Annals of Statistics) result. Numerical results are provided to study the risk performance of some of these improved mean estimators.
