Shrinkage estimation for square of location parameter of the exponential distribution with known coefficient of variation

被引：3

作者：

Singh, Housila P. ^{[1
]}

Vishwakarma, Gajendra K. ^{[2
]}

Joshi, Harshada ^{[1
]}

Gupta, Shubham ^{[2
]}

机构：

[1] Vikram Univ, Sch Studies Stat, Ujjain 456010, India

[2] Indian Inst Technol ISM Dhanbad, Dept Math & Comp, Dhanbad 826004, India

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2024年 / 437卷

关键词：

Exponential distribution; Coefficient of variation; Variance; Minimum mean square error estimator; Shrinkage estimation; SCALE PARAMETER; VARIANCE;

D O I：

10.1016/j.cam.2023.115489

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Shrinkage estimation has become a basic tool in the computational analysis of highdimensional data. Historically and conceptually a key development toward this was the discovery of the inadmissibility of the usual estimator of a multivariate normal mean. This study explores the problem of estimating the square of location parameter of an exponential distribution when the coefficient of variation is known without error. Several estimators have been proposed with their properties. The best unbiased estimator as well least minimum mean square error (MMSE) estimator has been identified among several estimators. Numerical illustrations are given in the support of the present study.& COPY; 2023 Elsevier B.V. All rights reserved.

引用

页数：10

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