Admissible estimators of binomial probability and the inverse bayes rule map
Copyright policy )Admissible estimators of binomial probability and the inverse bayes rule map
Let \(X\) be a binomial random variable with parameters n and \(\theta\), where \(\theta\) is an unknown binomial probability, and consider the Bayes estimation of \(\theta\) relative to a prior distribution \(\mu\). The authors obtain explicit formulae for the moments of the prior distribution through the values of the Bayes estimator, and use these to derive a new admissibility criterion.
- University of Massachusetts Amherst United States
- University of Massachusetts System United States
explicit formulae for the moments of the prior distribution, Bayes estimation, Bayesian inference, binomial probability, new admissibility criterion, Admissibility in statistical decision theory, inverse Bayes rule map
explicit formulae for the moments of the prior distribution, Bayes estimation, Bayesian inference, binomial probability, new admissibility criterion, Admissibility in statistical decision theory, inverse Bayes rule map
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