On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE) relative to the balanced loss function (BLF) is an...

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Bibliographic Details
Published in:Journal of mathematics (Hidawi) 2023, Vol.2023, p.1-12
Main Authors: Hamdaoui, Abdenour, Benkhaled, Abdelkader, Alshahrani, Mohammed, Terbeche, Mekki, Almutiry, Waleed, Alahmadi, Amani
Format: Article
Language:English
Subjects:
Covariance matrix
Estimators
Inequality
Mathematics
Polynomials
Random variables
Simulation
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Summary:This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE) relative to the balanced loss function (BLF) is analytically proved. We introduce a new class of shrinkage estimators which ameliorate the PPJSE, and then we construct a series of polynomial shrinkage estimators which improve the PPJSE; also, any estimator of this series can be ameliorated by adding to it a new term of higher degree. We end this paper by simulation studies which confirm the performance of the suggested estimators.
ISSN:2314-4629
2314-4785
DOI:10.1155/2023/5221061