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

被引：6

作者：

Zinodiny, S. ^{[1
]}

Rezaei, S. ^{[1
]}

Arjmand, O. Naghshineh ^{[1
]}

Nadarajah, S. ^{[2
]}

机构：

[1] Amirkabir Univ Technol, Dept Stat, Tehran, Iran

[2] Univ Manchester, Sch Math, Manchester, Lancs, England

来源：

STATISTICS & PROBABILITY LETTERS | 2013年 / 83卷 / 09期

关键词：

Bayes estimation; Minimax estimation; Multivariate normal mean; Quadratic loss function; Unknown variance; RIDGE-REGRESSION ESTIMATORS; UNKNOWN-VARIANCE;

D O I：

10.1016/j.spl.2013.05.021

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The problem of estimating the mean vector mu, of a multivariate normal distribution with the covariance matrix sigma(2) I-p, is considered under the loss function, (delta-mu)'D(delta-mu)/sigma(2), where sigma(2) is unknown and D is a known positive definite diagonal matrix. A large class of Bayes minimax estimators of mu is found. This class includes classes of estimators obtained by Lin and Mousa (1982) and Zinodiny et al. (2011). (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：2052 / 2056

页数：5

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