The problem of estimating the mean of a multivariate normal distribution is considered. A class of admissible minimax estimators is constructed. This class includes two well-known classes of estimators, Strawderman's and Alam's. Further, this class is much broader than theirs.
Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution
β Scribed by Hisayuki Hara
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 132 KB
- Volume
- 77
- Category
- Article
- ISSN
- 0047-259X
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β¦ Synopsis
It is well known that the best equivariant estimator of the variance covariance matrix of the multivariate normal distribution with respect to the full affine group of transformation is not even minimax. Some minimax estimators have been proposed. Here we treat this problem in the framework of a multivariate analysis of variance (MANOVA) model and give other classes of minimax estimators.
π SIMILAR VOLUMES
We consider confidence sets for the mean of a multivariate normal distribution with unknown covariance matrix of the form \_ 2 I. The coverage probability of the usual confidence set is shown to be improved asymptotically by centering at a shrinkage estimator.