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- Publisher
- Taylor & Francis
- Copyright
- Copyright Taylor & Francis Group, LLC
- ISSN
- 1532-415X
- eISSN
- 0361-0926
- DOI
- 10.1080/03610920701855038
- Publisher site
- See Article on Publisher Site
Abstract
Robust Bayesian analysis is connected with the effect of changing a prior within a class Γ instead of being specified exactly. The multiplicity of prior leads to a collection or a range of Bayes actions. It is interesting not only to investigate the range of estimators but also to recommend the optimal procedures. In this article, we deal with posterior regret Γ-minimax (PRGM) estimation and prediction of an unknown parameter θ and a value of a random variable Y under entropy loss function. Applications for k-records such as estimation and prediction problems are discussed.
Journal
Communications in Statistics: Theory and Methods – Taylor & Francis
Published: May 27, 2008
Keywords: Bayes estimator; Class of priors; Entropy loss function; Posterior regret Γ-minimax; Robust Bayesian estimation; 62C10; 62F10; 62F15; 62M20