2020•Communication in Statistics- Theory and MethodsRequires access

Φ admissibility of linear estimators of common mean parameter in general multivariate linear models under a balanced loss function

Mingxiang Cao, Junyong Park, Guangjun Shen

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Abstract

The definitions of Φ optimality and Φ admissibility of matrix common mean parameter are given in general multivariate linear models under a generalized matrix balanced loss function. We extend some previous studies to more general cases such that Φ admissibility of linear estimators on matrix common mean parameter. Sufficient and necessary conditions for linear estimators to be Φ admissible are obtained in classes of homogeneous and non homogeneous linear estimators, respectively.

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The definitions of Φ optimality and Φ admissibility of matrix common mean parameter are given in general multivariate linear models under a generalized matrix balanced loss function. We extend some previous studies to more general cases such that Φ admissibility of linear estimators on matrix common mean parameter. Sufficient and necessary conditions for linear estimators to be Φ admissible are obtained in classes of homogeneous and non homogeneous linear estimators, respectively.

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Available abstract

The definitions of Φ optimality and Φ admissibility of matrix common mean parameter are given in general multivariate linear models under a generalized matrix balanced loss function. We extend some previous studies to more general cases such that Φ admissibility of linear estimators on matrix common mean parameter. Sufficient and necessary conditions for linear estimators to be Φ admissible are obtained in classes of homogeneous and non homogeneous linear estimators, respectively.

Key concepts: Estimator, Mathematics, Applied mathematics, Multivariate statistics, Matrix (chemical analysis), Linear model, Homogeneous, Function (biology)

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