Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function
Copyright policy )Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function
Summary: In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.
posterior linearity, risk function, Bayes risk, Bayesian inference, Empirical decision procedures; empirical Bayes procedures, balanced loss function, posterior expected loss, finite population mean, empirical Bayes
posterior linearity, risk function, Bayes risk, Bayesian inference, Empirical decision procedures; empirical Bayes procedures, balanced loss function, posterior expected loss, finite population mean, empirical Bayes
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