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Admissibility of linear estimators for the stochastic regression coefficient in a general Gauss–Markoff model under a balanced loss function

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  • Cao, Mingxiang

Abstract

In this paper, problems of linearly admissible estimators for stochastic regression coefficients are considered in a general Gauss–Markoff model with random effects. The generalized balanced loss function is given, and under it the admissibility of linear estimators is investigated. Sufficient and necessary conditions for linear estimators to be admissible in classes of homogeneous and inhomogeneous linear estimators are obtained, separately.

Suggested Citation

  • Cao, Mingxiang, 2014. "Admissibility of linear estimators for the stochastic regression coefficient in a general Gauss–Markoff model under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 25-30.
    • Handle: RePEc:eee:jmvana:v:124:y:2014:i:c:p:25-30
    DOI: 10.1016/j.jmva.2013.10.015
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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. Dey, Dipak K. & Ghosh, Malay & Strawderman, William E., 1999. "On estimation with balanced loss functions," Statistics & Probability Letters, Elsevier, vol. 45(2), pages 97-101, November.
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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. Buatikan Mirezi & Selahattin Kaçıranlar, 2023. "Admissible linear estimators in the general Gauss–Markov model under generalized extended balanced loss function," Statistical Papers, Springer, vol. 64(1), pages 73-92, February.

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