Bayes minimax ridge regression estimators

被引:1
|
作者
Zinodiny, S. [1 ]
机构
[1] Inst Res Fundamental Sci IPM, Sch Math, POB 19395-5746, Tehran, Iran
关键词
Admissible estimation; Bayes estimation; Condition number; Minimax estimation; Multivariate normal distribution; Ridge regression; 62C10; 62C15; 62C20; NORMAL-MEAN VECTOR; CORRELATION INEQUALITIES;
D O I
10.1080/03610926.2017.1397167
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 of the vector of the linear regression model y = A + E with E approximate to N-p(0, sigma I-2(p)) under quadratic loss function is considered when common variance sigma(2) is unknown. We first find a class of minimax estimators for this problem which extends a class given by Maruyama and Strawderman (2005) and using these estimators, we obtain a large class of (proper and generalized) Bayes minimax estimators and show that the result of Maruyama and Strawderman (2005) is a special case of our result. We also show that under certain conditions, these generalized Bayes minimax estimators have greater numerical stability (i.e., smaller condition number) than the least-squares estimator.
引用
收藏
页码:5519 / 5533
页数:15
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