Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix
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DOI: 10.1016/j.jmva.2017.05.004
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References listed on IDEAS
- Faith, Ray E., 1978. "Minimax Bayes estimators of a multivariate normal mean," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 372-379, September.
- 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.
- Takeru Matsuda & Fumiyasu Komaki, 2015. "Singular value shrinkage priors for Bayesian prediction," Biometrika, Biometrika Trust, vol. 102(4), pages 843-854.
- 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.
- Zheng, Z., 1986. "Selecting a minimax estimator doing well at a point," Journal of Multivariate Analysis, Elsevier, vol. 19(1), pages 14-23, June.
- Zheng, Z., 1986. "On estimation of matrix of normal mean," Journal of Multivariate Analysis, Elsevier, vol. 18(1), pages 70-82, February.
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- 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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Keywords
Admissibility; Gauss’ divergence theorem; Generalized Bayes estimator; Inadmissibility; Kullback–Leibler loss; Minimaxity; Shrinkage estimator; Statistical decision theory;All these keywords.
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