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Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix

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  • Tsukuma, Hisayuki
  • Kubokawa, Tatsuya

Abstract

This paper deals with the problem of estimating predictive densities of a matrix-variate normal distribution with known covariance matrix. Our main aim is to establish some Bayesian predictive densities related to matricial shrinkage estimators of the normal mean matrix. The Kullback–Leibler loss is used for evaluating decision-theoretic optimality of predictive densities. It is shown that a proper hierarchical prior yields an admissible and minimax predictive density. Also, some minimax predictive densities are derived from superharmonicity of prior densities.

Suggested Citation

  • Tsukuma, Hisayuki & Kubokawa, Tatsuya, 2017. "Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 138-150.
    • Handle: RePEc:eee:jmvana:v:159:y:2017:i:c:p:138-150
    DOI: 10.1016/j.jmva.2017.05.004
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    References listed on IDEAS

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    1. Faith, Ray E., 1978. "Minimax Bayes estimators of a multivariate normal mean," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 372-379, September.
    2. Maruyama, Yuzo, 1998. "A Unified and Broadened Class of Admissible Minimax Estimators of a Multivariate Normal Mean," Journal of Multivariate Analysis, Elsevier, vol. 64(2), pages 196-205, February.
    3. Takeru Matsuda & Fumiyasu Komaki, 2015. "Singular value shrinkage priors for Bayesian prediction," Biometrika, Biometrika Trust, vol. 102(4), pages 843-854.
    4. Zheng, Z., 1986. "On estimation of matrix of normal mean," Journal of Multivariate Analysis, Elsevier, vol. 18(1), pages 70-82, February.
    5. Tsukuma, Hisayuki, 2008. "Admissibility and minimaxity of Bayes estimators for a normal mean matrix," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2251-2264, November.
    6. Zheng, Z., 1986. "Selecting a minimax estimator doing well at a point," Journal of Multivariate Analysis, Elsevier, vol. 19(1), pages 14-23, June.
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    Cited by:

    1. Malay Ghosh & Tatsuya Kubokawa & Gauri Sankar Datta, 2020. "Density Prediction and the Stein Phenomenon," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 330-352, August.

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