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ESTIMATING A BINOMIAL PARAMETER: IS ROBUST BAYES REAL BAYES?
Statistics & Risk Modeling, 1993Summary: In robust Bayesian analysis, a prior is assumed to belong to a family instead of being specified exactly. The multiplicity of priors naturally leads to a collection of Bayes actions (estimates), and these often form a convex set (an interval in the case of a real parameter).
Zen, Mei-Mei, DasGupta, A.
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Optimal robust Bayes estimation
Journal of Statistical Planning and Inference, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Yuanzhang, Saxena, K. M. Lal
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Linear Bayes and Optimal Estimation
Annals of the Institute of Statistical Mathematics, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bayes Estimates Under Bounded Loss
Biometrika, 1980SUMMARY Regions conitaining all possible Bayes estimates under certain classes of bounded loss functions are given for many standard distributions. The properties of such regions are studied in depth for general classes of posterior distributions. Estimators are proposed that replace the posterior mean.
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Stein's positive part estimator and bayes estimator
Annals of the Institute of Statistical Mathematics, 1979Stein's positive part estimator forp normal means is known to dominate the M.L.E. ifp≧3. In this article by introducing some proirs we show that Stein's positive part estimator is posterior mode. We also consider the Bayes estimators (posterior mean) with respect to the same priors and show that some of them dominate M.L.E. and are admissible.
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Sensitivity of bayes and empirical bayes estimates
Communications in Statistics - Theory and Methods, 1990The main objective is to investigate the behaviour of Bayes and empirical Bayes confidence intervals for a mean to changes from normality in the specification of either the sampling or prior distributions. To do this the posterior mean and variance are calculated when the prior and sampling distributions are defined by Edgeworth expansions, corrective ...
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ESTIMATES OF MULTIPLE POISSON MEANS: BAYES AND EMPIRICAL BAYES
Statistics & Risk Modeling, 1983Summary: For estimating multiple Poisson means, Bayes and empirical Bayes estimates are proposed. Such estimates, under suitable loss, sometimes dominate the usual maximum likelihood estimates. A study of the ''relative savings loss'' of such estimates as compared to maximum likelihood estimates is also made using a Bayesian viewpoint.
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