Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Volume 437, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cam.2023.115489
Keywords
Exponential distribution; Coefficient of variation; Variance; Minimum mean square error estimator; Shrinkage estimation
Categories
Ask authors/readers for more resources
Shrinkage estimation is a fundamental tool in analyzing high-dimensional data. This study focuses on estimating the square of the location parameter in an exponential distribution when the coefficient of variation is known without error. Various estimators are proposed and compared, and the best unbiased estimator and the minimum mean square error estimator are identified. Numerical illustrations are provided to support the findings of this study.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available