On estimation of a matrix of normal means with unknown covariance matrix
AbstractLet X be an m - p matrix normally distributed with matrix of means B and covariance matrix Im [circle times operator] [Sigma], where [Sigma] is a p - p unknown positive definite matrix. This paper studies the estimation of B relative to the invariant loss function tr . New classes of invariant minimax estimators are proposed for the case p > m + 1, which are multivariate extensions of the estimators of Stein and Baranchik. The method involves the unbiased estimation of the risk of an invariant estimator which depends on the eigenstructure of the usual F = XS-1Xt matrix, where S: p - p follows a Wishart matrix with n degrees of freedom and mean n[Sigma].
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 36 (1991)
Issue (Month): 1 (January)
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- Tatsuya Kubokawa & M. S. Srivastava, 2002. "Minimax Multivariate Empirical Bayes Estimators under Multicollinearity," CIRJE F-Series CIRJE-F-187, CIRJE, Faculty of Economics, University of Tokyo.
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