2023Journal of the Korean Data and Information Science SocietyRequires access

An improvement of James-Stein estimator under the balanced loss function

In‐Bong Choi, Hoh-Yoo Baek, Jeong-Mi Lee

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Abstract

In this paper we are dealing with the shrinkage estimators of a multivariate normal mean and their minimaxity properties under the balanced loss function. This paper is presented here two different classes of estimator. First, we generalize the James-Stein estimator and show that any estimator of this class dominates the usual estimator. Second, we can also show that it dominates the James-Stein estimator and conclude that any estimator of this class is minimax.

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What this paper is about

In this paper we are dealing with the shrinkage estimators of a multivariate normal mean and their minimaxity properties under the balanced loss function. This paper is presented here two different classes of estimator. First, we generalize the James-Stein estimator and show that any estimator of this class dominates the usual estimator. Second, we can also show that it dominates the James-Stein estimator and conclude that any estimator of this class is minimax.

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Available abstract

In this paper we are dealing with the shrinkage estimators of a multivariate normal mean and their minimaxity properties under the balanced loss function. This paper is presented here two different classes of estimator. First, we generalize the James-Stein estimator and show that any estimator of this class dominates the usual estimator. Second, we can also show that it dominates the James-Stein estimator and conclude that any estimator of this class is minimax.

Key concepts: James–Stein estimator, Invariant estimator, Estimator, Minimum-variance unbiased estimator, Stein's unbiased risk estimate, Efficient estimator, Minimax estimator, Mathematics

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