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empirical bayes estimators
First, in all cases the empirical Bayes estimators improve upon OLS.
      
Only in this case did choosing the regressors by BIC perform better than the empirical Bayes estimators.
      
Results are also shown for the empirical Bayes estimators and for the DIAR estimator.
      
Section 7 summarizes a Monte Carlo study of the Gaussian empirical Bayes estimators from both Bayesian and frequentist perspectives.
      
Second, the relative MSE of the empirical Bayes estimators are always less than 1.0, so that these forecasts improve on the univariate autoregression.
      
Taken as a whole the table suggests only modest improvement of the empirical Bayes estimators relative to the univariate autoregression.
      
Two of our empirical Bayes estimators in section 3.2 permit phase alteration and perform extremely well in our simulation study in section 4.
      
Third, as in the Monte Carlo experiment, the parametric and non-parametric empirical Bayes estimators have nearly identical performance.
      
This is somewhat surprising given the performance of the Empirical Bayes estimators in the Monte Carlo experiments reported in section 4.3.
      
This bound is achieved by the empirical Bayes estimators laid out in this paper.
      
The Model, Bayes Risk, and Gaussian Empirical Bayes Estimators 2.1.
      
The model, Bayesian risk function, and Gaussian empirical Bayes estimators are presented in section 2.
      
 

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