2010Unpublished venueRequires access

SHRINKAGE PRE-TEST ESTIMATOR OF THE UNIVARIATE NORMAL MEAN

Güzin Yüksel, Nedret Billor, Deniz Ünal

Open publisher page 1 citations

Abstract

Estimation of the mean of a univariate normal population with unknown variance is a well-known problem in the presence of an uncertain prior information. In this study, we propose a new estimator, shrinkage pre-test estimator, which is a combination of pre-test and shrinkage estimators. The mean squared error (MSE) for the shrinkage pre-test estimator is also derived and theoretical comparisons based on MSE criterion of this new estimator with the restricted, the pre-test, and the shrinkage estimators are given. We show that the shrinkage pre-test estimator performs better than the other existing estimators. In addition, the performance of this newly proposed estimator is being assessed by conducting a simulation study.

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What this paper is about

Estimation of the mean of a univariate normal population with unknown variance is a well-known problem in the presence of an uncertain prior information. In this study, we propose a new estimator, shrinkage pre-test estimator, which is a combination of pre-test and shrinkage estimators. The mean squared error (MSE) for the shrinkage pre-test estimator is also derived and theoretical comparisons based on MSE criterion of this new estimator with the restricted, the pre-test, and the shrinkage estimators are given. We show that the shrinkage pre-test estimator performs better than the other existing estimators. In addition, the performance of this newly proposed estimator is being assessed by conducting a simulation study.

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Available abstract

Estimation of the mean of a univariate normal population with unknown variance is a well-known problem in the presence of an uncertain prior information. In this study, we propose a new estimator, shrinkage pre-test estimator, which is a combination of pre-test and shrinkage estimators. The mean squared error (MSE) for the shrinkage pre-test estimator is also derived and theoretical comparisons based on MSE criterion of this new estimator with the restricted, the pre-test, and the shrinkage estimators are given. We show that the shrinkage pre-test estimator performs better than the other existing estimators. In addition, the performance of this newly proposed estimator is being assessed by conducting a simulation study.

Key concepts: Shrinkage estimator, Estimator, Shrinkage, Mean squared error, Statistics, Mathematics, Minimum-variance unbiased estimator, Efficient estimator

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