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All admissible linear estimators of a regression coefficient under a balanced loss function

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  • Hu, Guikai
  • Peng, Ping

Abstract

Admissibility of linear estimators of a regression coefficient in linear models with and without the assumption that the underlying distribution is normal is discussed under a balanced loss function. In the non-normal case, a necessary and sufficient condition is given for linear estimators to be admissible in the space of homogeneous linear estimators. In the normal case, a sufficient condition is provided for restricted linear estimators to be admissible in the space of all estimators having finite risks under the balanced loss function. Furthermore, the sufficient condition is proved to be necessary in the normal case if additional conditions are assumed.

Suggested Citation

  • Hu, Guikai & Peng, Ping, 2011. "All admissible linear estimators of a regression coefficient under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 102(8), pages 1217-1224, September.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:8:p:1217-1224
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    References listed on IDEAS

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    1. Jafari Jozani, Mohammad & Marchand, Éric & Parsian, Ahmad, 2006. "On estimation with weighted balanced-type loss function," Statistics & Probability Letters, Elsevier, vol. 76(8), pages 773-780, April.
    2. Ohtani Kazuhiro, 1998. "The Exact Risk Of A Weighted Average Estimator Of The Ols And Stein-Rule Estimators In Regression Under Balanced Loss," Statistics & Risk Modeling, De Gruyter, vol. 16(1), pages 35-46, January.
    3. Kazuhiro Ohtani & David Giles & Judith Giles, 1997. "The exact risk performance of a pre-test estimator in a heteroskedastic linear regression model under the balanced loss function," Econometric Reviews, Taylor & Francis Journals, vol. 16(1), pages 119-130.
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    Cited by:

    1. Cao, Ming-Xiang & He, Dao-Jiang, 2017. "Admissibility of linear estimators of the common mean parameter in general linear models under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 246-254.
    2. Hu, Guikai & Peng, Ping, 2012. "Matrix linear minimax estimators in a general multivariate linear model under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 286-295.
    3. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    4. Hu, Guikai & Li, Qingguo & Yu, Shenghua, 2014. "Optimal and minimax prediction in multivariate normal populations under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 154-164.
    5. He, Daojiang & Wu, Jie, 2014. "Admissible linear estimators of multivariate regression coefficient with respect to an inequality constraint under matrix balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 37-43.
    6. Marchand, Éric & Strawderman, William E., 2020. "On shrinkage estimation for balanced loss functions," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    7. Zinodiny, S. & Rezaei, S. & Nadarajah, S., 2014. "Bayes minimax estimation of the multivariate normal mean vector under balanced loss function," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 96-101.

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