The best uniform convergence rate of linear empirical bayes estimators

1992 ◽  
Vol 8 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Tao Bo
Keyword(s):  
Convergence Rate ◽  
Uniform Convergence ◽  
Empirical Bayes ◽  
Bayes Estimators ◽  
Uniform Convergence Rate ◽  
Empirical Bayes Estimators

Data Processing and Technology Application in Bayes and Empirical Bayes Reliability Analysis of Parameter of Ailamujia Distribution

2014 ◽  
Vol 951 ◽  
pp. 249-252
Author(s):  
Hui Zhou
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimators ◽  
Conjugate Prior ◽  
Likelihood Estimator ◽  
Empirical Bayesian ◽  
Technology Application ◽  
Inverse Gamma ◽  
Squared Error ◽  
Linex Loss ◽  
Empirical Bayes Estimators

The estimation of the parameter of the ЭРланга distribution is discussed based on complete samples. Bayes and empirical Bayesian estimators of the parameter of the ЭРланга distribution are obtained under squared error loss and LINEX loss by using conjugate prior inverse Gamma distribution. Finally, a Monte Carlo simulation example is used to compare the Bayes and empirical Bayes estimators with the maximum likelihood estimator.

scholarly journals Dominance properties of constrained Bayes and empirical Bayes estimators

Bernoulli ◽  
2013 ◽  
Vol 19 (5B) ◽  
pp. 2200-2221 ◽  
Author(s):  
Tatsuya Kubokawa ◽  
William E. Strawderman
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimators ◽  
Constrained Bayes ◽  
Empirical Bayes Estimators ◽  
Dominance Properties

Robustness to Parametric Assumptions in Missing Data Models

2011 ◽  
Vol 101 (3) ◽  
pp. 538-543 ◽  
Author(s):  
Bryan S Graham ◽  
Keisuke Hirano
Keyword(s):  
Empirical Bayes ◽  
Missing At Random ◽  
Data Driven ◽  
Robust Estimator ◽  
Sampling Variance ◽  
Bayes Estimators ◽  
Cell Means ◽  
Double Robust ◽  
Empirical Bayes Estimators ◽  
Empirical Bayes Estimator

We consider estimation of population averages when data are missing at random. If some cells contain few observations, there can be substantial gains from imposing parametric restrictions on the cell means, but there is also a danger of misspecification. We develop a simple empirical Bayes estimator, which combines parametric and unadjusted estimates of cell means in a data-driven way. We also consider ways to use knowledge of the form of the propensity score to increase robustness. We develop an empirical Bayes extension of a double robust estimator. In a small simulation study, the empirical Bayes estimators perform well. They are similar to fully nonparametric methods and robust to misspecification when cells are moderate to large in size, and when cells are small they maintain the benefits of parametric methods and can have lower sampling variance.

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