An improvement of James-Stein estimator under the balanced loss function
In‐Bong Choi, Hoh-Yoo Baek, Jeong-Mi Lee
Abstract
In‐Bong Choi, Hoh-Yoo Baek, Jeong-Mi Lee
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.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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