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Admissible linear estimators of multivariate regression coefficient with respect to an inequality constraint under matrix balanced loss function

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  • He, Daojiang
  • Wu, Jie

Abstract

In this paper, the admissibility of linear estimators for the multivariate linear regression coefficient with respect to an inequality constraint under matrix balanced loss function is investigated. The sufficient and necessary conditions for admissible homogeneous and inhomogeneous linear estimators are obtained, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:37-43
    DOI: 10.1016/j.jmva.2014.04.005
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    References listed on IDEAS

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    1. 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.
    2. Rao, C. Radhakrishna, 1973. "Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix," Journal of Multivariate Analysis, Elsevier, vol. 3(3), pages 276-292, September.
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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. Peng, Ping & Hu, Guikai & Liang, Jian, 2015. "All admissible linear predictors in the finite populations with respect to inequality constraints under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 113-122.

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