Empirical Bayes minimax estimators of matrix normal means
AbstractThe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 38 (1991)
Issue (Month): 2 (August)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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.
- 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.
- 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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