General Admissibility for Linear Estimators in a General Multivariate Linear Model under Balanced Loss Function

A generalization of Zellner’s balanced loss function is proposed. General admissibility in a general multivariate linear model is investigated under the generalized balanced loss function. And the sufficient and necessary conditions for linear estimators to be generally admissible in classes of homo...

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Bibliographic Details
Published inActa mathematica Sinica. English series Vol. 29; no. 9; pp. 1823 - 1832
Main Authors Cao, Ming Xiang, Kong, Fan Chao
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2013
Springer Nature B.V
Subjects
Balancing
Estimators
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Studies
Theorems
一般化
充分必要条件
多元线性模型
容许
平衡
损失函数
线性估计类
非齐次
Balanced loss function
62J05
general optimality
62C15
general admissibility
linear estimators
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Summary:A generalization of Zellner’s balanced loss function is proposed. General admissibility in a general multivariate linear model is investigated under the generalized balanced loss function. And the sufficient and necessary conditions for linear estimators to be generally admissible in classes of homogeneous and nonhomogeneous linear estimators are given, respectively.
Bibliography:Balanced loss function; linear estimators; general optimality; general admissibility
A generalization of Zellner’s balanced loss function is proposed. General admissibility in a general multivariate linear model is investigated under the generalized balanced loss function. And the sufficient and necessary conditions for linear estimators to be generally admissible in classes of homogeneous and nonhomogeneous linear estimators are given, respectively.
Ming Xiang CAO,Fan Chao KONG（Department of Mathematics, Hefei Normal University, Hefei 230601, P. R. China;School of Mathematics, Anhui University, Hefei 230039, P. R. China）
11-2039/O1
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1439-8516
1439-7617
DOI:10.1007/s10114-013-9246-3