On minimaxity and admissibility of hierarchical Bayes estimators
Copyright policy )On minimaxity and admissibility of hierarchical Bayes estimators
AbstractThis paper obtains conditions for minimaxity of hierarchical Bayes estimators in the estimation of a mean vector of a multivariate normal distribution. Hierarchical prior distributions with three types of second stage priors are treated. Conditions for admissibility and inadmissibility of the hierarchical Bayes estimators are also derived using the arguments in Berger and Strawderman [Choice of hierarchical priors: admissibility in estimation of normal means, Ann. Statist. 24 (1996) 931–951]. Combining these results yields admissible and minimax hierarchical Bayes estimators.
- Rutgers, The State University of New Jersey United States
- University of Tokyo Japan
Statistics and Probability, Numerical Analysis, Shrinkage estimation, Admissibility, Inadmissibility, Minimaxity, Mixed linear model, Bayes inference, Multivariate normal distribution, Hierarchical Bayes model, Statistics, Probability and Uncertainty, Estimation
Statistics and Probability, Numerical Analysis, Shrinkage estimation, Admissibility, Inadmissibility, Minimaxity, Mixed linear model, Bayes inference, Multivariate normal distribution, Hierarchical Bayes model, Statistics, Probability and Uncertainty, Estimation
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