  Parametric empirical Bayes (EB) estimators have been widely used in variety
of fields including small area estimation, disease mapping. Since EB estimator
is constructed by plugging in the estimator of parameters in prior
distributions, it might perform poorly if the estimator of parameters is
unstable. This can happen when the number of samples are small or moderate.
This paper suggests bootstrapping averaging approach, known as "bagging" in
machine learning literatures, to improve the performances of EB estimators. We
consider two typical hierarchical models, two-stage normal hierarchical model
and Poisson-gamma model, and compare the proposed method with the classical
parametric EB method through simulation and empirical studies.
