Bayes minimax estimation of the multivariate normal mean vector under quadratic loss functions

The problem of estimating the mean vector μ of a multivariate normal distribution with the covariance matrix σ2Ip is considered under the loss function, (δ−μ)′D(δ−μ)σ2, where σ2 is unknown and D is a known positive definite diagonal matrix. A large class of Bayes minimax estimators of μ is found. This class includes classes of estimators obtained by Lin and Mousa (1982) and Zinodiny et al. (2011).

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Year of publication:	2013
Authors:	Zinodiny, S. ; Rezaei, S. ; Arjmand, O. Naghshineh ; Nadarajah, S.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 9, p. 2052-2056
Publisher:	Elsevier
Subject:	Bayes estimation \| Minimax estimation \| Multivariate normal mean \| Quadratic loss function \| Unknown variance

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

Persistent link: https://www.econbiz.de/10011040093