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Empirical Bayes minimax estimators of matrix normal means

Author

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  • Ghosh, Malay
  • Shieh, Gwowen

Abstract

The paper considers estimation of matrix normal means. A class of empirical Bayes estimators is proposed which dominates the maximum likelihood estimator simultaneously for many quadratic losses. Several of these empirical Bayes estimators are compared in terms of their simulated risks, and a concrete recommendation is made about the choice of a particular empirical Bayes estimator.

Suggested Citation

  • Ghosh, Malay & Shieh, Gwowen, 1991. "Empirical Bayes minimax estimators of matrix normal means," Journal of Multivariate Analysis, Elsevier, vol. 38(2), pages 306-318, August.
  • Handle: RePEc:eee:jmvana:v:38:y:1991:i:2:p:306-318
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    Citations

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    Cited by:

    1. Tsukuma, Hisayuki, 2009. "Generalized Bayes minimax estimation of the normal mean matrix with unknown covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2296-2304, November.
    2. Pensky, Marianna, 1999. "Nonparametric Empirical Bayes Estimation of the Matrix Parameter of the Wishart Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 242-260, May.
    3. Pensky Marianna, 2002. "A New Approach To Empirical Bayes Estimation With Errors In Variables," Statistics & Risk Modeling, De Gruyter, vol. 20(1-4), pages 225-240, April.
    4. Tsukuma, Hisayuki & Kubokawa, Tatsuya, 2007. "Methods for improvement in estimation of a normal mean matrix," Journal of Multivariate Analysis, Elsevier, vol. 98(8), pages 1592-1610, September.
    5. Hisayuki Tsukuma & Tatsuya Kubokawa, 2005. "Methods for Improvement in Estimation of a Normal Mean Matrix," CIRJE F-Series CIRJE-F-378, CIRJE, Faculty of Economics, University of Tokyo.

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