Results 251 to 260 of about 6,276,816 (299)

Sensitivity of bayes and empirical bayes estimates

Communications in Statistics - Theory and Methods, 1990
The 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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Bayes Estimates of Haplotype Effects

Genetic Epidemiology, 2001
We describe a Markov chain Monte Carlo implementation of a Bayesian approach to estimating associations of a trait with a large set of haplotypes recently introduced by Clayton and Jones [Am J Hum Genet 65:1161–9, 2000]. The model uses the length of the longest segment in common between any two haplotypes to define the prior correlation structure for ...
D C, Thomas, J L, Morrison, D G, Clayton
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ESTIMATING A BINOMIAL PARAMETER: IS ROBUST BAYES REAL BAYES?

Statistics & Risk Modeling, 1993
Summary: 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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Bayes estimation of a convex quadratic

Biometrika, 1973
SUMMARY The estimation of a quadratic regression curve when the quadratic coefficient is known to be positive is treated by a Bayesian method. The method extends easily to deal with a general linear model under parameter constraints.
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Limiting the Risk of Bayes and Empirical Bayes Estimators--Part I: The Bayes Case

Journal of the American Statistical Association, 1971
Abstract The first part of this article considers the Bayesian problem of estimating the mean, θ, of a normal distribution when the mean itself has a normal prior. The usual Bayes estimator for this situation has high risk if θ is far from the mean of the prior distribution.
Bradley Efron, Carl Morris
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ON THE CONSISTENCY OF HIERARCHICAL BAYES ESTIMATORS

Statistics & Risk Modeling, 1996
Summary: In considering Bayesian estimation of multivariate normal mean, \textit{G. S. Datta} and \textit{M. Ghosh} [J. Stat. Plann. Inference 29, No. 3, 229-243 (1991; Zbl 0756.62014)] proposed hierarchical Bayes estimators and studied their asymptotic optimality property. A conjecture was raised therein.
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Bayes and empirical Bayes estimation with errors in variables

Statistics & Probability Letters, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Shunpu, Karunamuni, Rohana J.
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Linear Bayes and Optimal Estimation

Annals of the Institute of Statistical Mathematics, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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ESTIMATES OF MULTIPLE POISSON MEANS: BAYES AND EMPIRICAL BAYES

Statistics & Risk Modeling, 1983
Summary: 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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Stein's positive part estimator and bayes estimator

Annals of the Institute of Statistical Mathematics, 1979
Stein'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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