On combining unbiased and possibly biased correlated estimators

被引：0

作者：

Zinonos, Stavros ^{[1
]}

Strawderman, William E. ^{[2
]}

机构：

[1] RWJMS, Cardiovasc Inst New Jersey, New Brunswick, NJ 08901 USA

[2] Rutgers State Univ, New Brunswick, NJ USA

来源：

JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE | 2024年 / 7卷 / 01期

关键词：

62J07; 62F10; 62C20; 62G05; MINIMAX ESTIMATION; LOCATION VECTORS;

D O I：

10.1007/s42081-023-00194-2

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：

We study estimators that combine an unbiased estimator with a possibly biased correlated estimator of a mean vector. The combined estimators are shrinkage-type estimators that shrink the unbiased estimator towards the biased estimator. Conditions under which the combined estimator dominates the original unbiased estimator are given. Models studied include normal models with a known covariance structure, scale mixtures of normals, and more generally elliptically symmetric models with a known covariance structure. Elliptically symmetric models with a covariance structure known up to a multiple are also considered.

引用

页码：465 / 505

页数：41