Admissibilities of matrix linear estimators multivariate linear models

被引:5
|
作者
Wu, Qi-Guang
Noda, Kazuo
机构
[1] Acad Sinica, Inst Syst Sci, Beijing 100080, Peoples R China
[2] Meisei Univ, Fac Sci & Technol, Tokyo 1918506, Japan
基金
中国国家自然科学基金;
关键词
estimable parameter matrix linear function; with and without normality assumption; unknown covariance matrix; necessary and sufficient conditions; quadratic matfix loss functions; space of all matrix estimators; restricted space of all matrix linear estimators;
D O I
10.1016/j.jspi.2005.05.001
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
This article respectively provides sufficient conditions and necessary conditions of matrix linear estimators of an estimable parameter matrix linear function in multivariate linear models with and without the assumption that the underlying distribution is a normal one with completely unknown covariance matrix. In the latter model, a necessary and sufficient condition is given for matrix linear estimators to be admissible in the space of all matrix linear estimators under each of three different kinds of quadratic matrix loss functions, respectively. In the former model, a sufficient condition is first provided for matrix linear estimators to be admissible in the space of all matrix estimators having finite risks under each of the same loss functions, respectively. Furthermore in the former model, one of these sufficient conditions, correspondingly under one of the loss functions, is also proved to be necessary, if additional conditions are assumed. (c) 2005 Elsevier B.V. All rights reserved.
引用
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页码:3852 / 3870
页数:19
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