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empirical bayes estimator
In [1] the empirical Bayes estimator of the parameter vector of normal distribution family was introduced, and for the loss function (1) its asymptotically optimal property was proved with respect to the prior distribution family (2).
      
In this paper the thought on the uniform convergence of an empirical Bayes estimator or linear empirical Bayes (l.e.B.) estimator is advanced.
      
In this setting, we consider PRL reliability measures based on two estimators of the correct category-the empirical Bayes estimator and an estimator based on the judges' consensus choice.
      
The results show the best behaviour of Empirical Bayes estimator (EB).
      
It is established that the empirical Bayes estimator is inadmissible and the improved estimators are also derived.
      
One of our estimators, an empirical Bayes estimator, is seen to consistently obtain substantially lower RMSE overall.
      
Specifically, an Empirical Bayes' estimator as described by Hedges and Olkin6 was used in this metaanalysis.
      
This methodology used an empirical Bayes estimator in a random effects model.
      
The empirical Bayes estimator for the selected scale parameters are obtained.
      
We used the R packages waveslim to obtain the wavelet decompositions and ebayesthresh to compute the empirical Bayes estimator.
      
 

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