A01=J. S. Maritz
A01=T. Lwin
Asymptotic Optimality
Author_J. S. Maritz
Author_T. Lwin
Bayes Decision Rule
Bayes Point Estimate
Bayes Rule
bayesian applications
Bayesian decision theory
bayesian economics
Bayesian Inferential Approach
bayesian mathematics
bayesian methods
bayesian models
Bayesian Region
Binomial Kernel
Category=KCH
Coverage Probability
Data Set
EB Analysis
EB Approach
EB Estimation
EB Estimator
EB Method
empirical bayes
empirical bayes economics
empirical Bayes estimator performance assessment
Empirical Bayes methods with applications
empirical bayes models
Empirical Bayes Point Estimation
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Fb Approach
Finite Mixtures
hypothesis testing statistics
interval estimation techniques
J.S. Maritz
James Stein Estimator
Linear Bayes Estimator
mathematics economics
Ml Estimation
Natural Conjugate Prior
Normal Data Distribution
point estimation methods
Posterior Distribution
Prior Distribution
prior distribution estimation
Special Loss Function
statistical inference
T. Lwin
Unknown Prior Distribution
Product details
- ISBN 9780815350378
- Weight: 550g
- Dimensions: 138 x 216mm
- Publication Date: 25 Jun 2019
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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Originally published in 1970; with a second edition in 1989. Empirical Bayes methods use some of the apparatus of the pure Bayes approach, but an actual prior distribution is assumed to generate the data sequence. It can be estimated thus producing empirical Bayes estimates or decision rules.
In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. Chapters also focus on alternatives to the empirical Bayes approach and actual applications of empirical Bayes methods.
