Admissible Minimax Estimation of a Multivariate Normal Mean with Arbitrary Quadratic Loss

By James O. Berger. 1976

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1. Lawrence D. Brown
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4. Zhuang Ma
5. Tommaso Cai
6. Peter Buhlmann
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8. Sara A. van de Geer

Citations in the corpus

• Kernel Mean Shrinkage Estimators (2014, positive): “Interestingly, the James-Stein estimator is itself inadmissible, and there exists a wide class of estimators that outperform the MLE, see, e.g. , Berger (1976).”
• Kernel Mean Estimation and Stein's Effect (2013, neutral): “1”
• Group-Linear Empirical Bayes Estimates for a Heteroscedastic Normal Mean (2015, methodology): “he usual empirical Bayes estimators, that use Maximum Likelihood or Method-of-Moments estimates of ; under (5). On the other hand, Berger (1982) suggested a modification of his own minimax estimator (Berger, 1976) inspired by an approximate robust Bayes estimator (Berger, 1980), that improves Bayesia”
• A new class of generalized Bayes minimax ridge regression estimators (2004, unknown): “ar X iv :m at h/ 05 08 28 2v 1 [ m at h. ST ] 1 6 A ug 2 00 5 The Annals of Statistics 2005, Vol. 33, No. 4, 1753–1770 DOI: 10.1214/009053605000000327 c© Institute of Mathematical Statistics, 2005”