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2014 Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
Guikai Hu, Qingguo Li, Shenghua Yu
J. Appl. Math. 2014: 1-7 (2014). DOI: 10.1155/2014/129205

Abstract

Under a balanced loss function, we derive the explicit formulae of the risk of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the feasible minimum mean squared error (FMMSE) estimator, and the adjusted feasible minimum mean squared error (AFMMSE) estimator in a linear regression model with multivariate t errors. The results show that the PSR estimator dominates the SR estimator under the balanced loss and multivariate t errors. Also, our numerical results show that these estimators dominate the ordinary least squares (OLS) estimator when the weight of precision of estimation is larger than about half, and vice versa. Furthermore, the AFMMSE estimator dominates the PSR estimator in certain occasions.

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Guikai Hu. Qingguo Li. Shenghua Yu. "Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function." J. Appl. Math. 2014 1 - 7, 2014. https://doi.org/10.1155/2014/129205

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131334
MathSciNet: MR3208614
Digital Object Identifier: 10.1155/2014/129205

Rights: Copyright © 2014 Hindawi

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