ScholarWorks@숙명여자대학교

초록

In this paper, we propose the nonparametric empirical Bayes (NPEB) estimator based on the nonparametric maximum likelihood estimation (NPMLE) in Poisson mean vector estimation, also known as the g-modeling in the nonparametric empirical Bayes method. Due to the recent developments of highly scalable algorithms of empirical Bayes, it is more attractive to use g-modelling, while most of the studies have focused on the performance of f-modeling in the NPEB estimator. We study the theoretical properties of the NPEB estimator of Poisson mean vector based on g-modeling combined with the NPMLE, such as the convergence rate, and compare our result with some existing studies. Our simulation studies and real data examples of protein domain data show that the estimator based on the g-modeling outperforms existing f-modeling based estimators in both computational efficiency and accuracy.

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