Generalized Bayes minimax estimation of the normal mean matrix with unknown covariance matrix

This paper addresses the problem of estimating the normal mean matrix in the case of unknown covariance matrix. This problem is solved by considering generalized Bayesian hierarchical models. The resulting generalized Bayes estimators with respect to an invariant quadratic loss function are shown to be matricial shrinkage equivariant estimators and the conditions for their minimaxity are given.

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Year of publication:	2009
Authors:	Tsukuma, Hisayuki
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 100.2009, 10, p. 2296-2304
Publisher:	Elsevier
Keywords:	Decision theory Equivariance Generalized Bayes estimation Hierarchical model Minimaxity Multivariate linear model Posterior mean Quadratic loss Shrinkage estimator

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008521102