Empirical Likelihood Based Posterior Expectation: from nonparametric posterior means via double empirical Bayesian estimators to nonparametric versions of the James-Stein estimator
نویسنده
چکیده
Posterior expectation is a well-accepted method for data analysis via Bayesian inference based on parametric likelihoods. In this paper we propose utilizing empirical likelihood (EL) methodology to develop novel nonparametric posterior expectation. The parametric Bayesian methodology contains the empirical Bayes approach for the purpose of using the observed data to estimate parameters, or even functional forms, of prior distributions. We adapt this approach to nonpara15
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