MINIMAX ESTIMATION OF A MULTIVARIATE NORMAL MEAN UNDER A CONVEX LOSS FUNCTION

1983 ◽  
Vol 25 (3) ◽  
pp. 463-466 ◽  
Author(s):  
Pi-Erh Lin ◽  
Amany Mousa
Keyword(s):  
Loss Function ◽  
Minimax Estimation ◽  
Multivariate Normal ◽  
Multivariate Normal Mean ◽  
Convex Loss Function ◽  
Normal Mean ◽  
Convex Loss

scholarly journals Limits of Risks Ratios of Shrinkage Estimators under the Balanced Loss Function

2021 ◽  
pp. 301-312
Author(s):  
Mekki Terbeche
Keyword(s):  
Maximum Likelihood ◽  
Loss Function ◽  
Sufficient Conditions ◽  
Maximum Likelihood Estimators ◽  
Shrinkage Estimators ◽  
Multivariate Normal ◽  
Balanced Loss Function ◽  
Multivariate Normal Mean ◽  
Normal Mean ◽  
Stein Estimator

In this paper we study the estimation of a multivariate normal mean under the balanced loss function. We present here a class of shrinkage estimators which generalizes the James-Stein estimator and we are interested to establish the asymptotic behaviour of risks ratios of these estimators to the maximum likelihood estimators (MLE). Thus, in the case where the dimension of the parameter space and the sample size are large, we determine the sufficient conditions for that the estimators cited previously are minimax

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