This article is intended as an expositional overview of empirical Bayes modeling methodology, presented in a simplified framework that reduces technical difficulties. The two principal empirical Bayes approaches, called f -modeling and g-modeling here, are described and compared. A series of computational formulas are developed to assess the frequentist accuracy of empirical Bayes applications....

Based on progressively Type-II censored samples, the uniformly minimum variance unbiased (UMVU), Bayes and empirical Bayes estimates for the unknown parameter and the reliability function of the Burr model are derived. The Bayes and empirical Bayes estimates are obtained based on absolute error and logarithmic loss functions. We also present a numerical example and a Monte Carlo simulation stud...

We consider the problem of forecasting multi-class job flow times in a resource-sharing environment. We assume the deviation of flow times in each class from the class nominal value follows an exponential distribution with its parameter following a gamma distribution. A large simulation experiment is conducted to assess and compare the performance of the Bayes and empirical Bayes forecasting me...

We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes...