Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function

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 r...

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Main Authors: Guikai Hu, Qingguo Li, Shenghua Yu
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/129205
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author Guikai Hu
Qingguo Li
Shenghua Yu
author_facet Guikai Hu
Qingguo Li
Shenghua Yu
author_sort Guikai Hu
collection DOAJ
description 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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publishDate 2014-01-01
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series Journal of Applied Mathematics
spelling doaj-art-0cf4ef5f455d4f788b869699ea4cf31e2025-08-20T03:54:15ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/129205129205Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss FunctionGuikai Hu0Qingguo Li1Shenghua Yu2School of Mathematics and Econometrics, Hunan University, Changsha 410082, ChinaSchool of Mathematics and Econometrics, Hunan University, Changsha 410082, ChinaSchool of Economics and Trade, Hunan University, Changsha 410079, ChinaUnder 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.http://dx.doi.org/10.1155/2014/129205
spellingShingle Guikai Hu
Qingguo Li
Shenghua Yu
Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
Journal of Applied Mathematics
title Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
title_full Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
title_fullStr Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
title_full_unstemmed Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
title_short Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function
title_sort risk comparison of improved estimators in a linear regression model with multivariate t errors under balanced loss function
url http://dx.doi.org/10.1155/2014/129205
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AT shenghuayu riskcomparisonofimprovedestimatorsinalinearregressionmodelwithmultivariateterrorsunderbalancedlossfunction