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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/129205 |
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| Summary: | 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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| ISSN: | 1110-757X 1687-0042 |