Empirical Bayes Rules for Selecting the Best Binomial Population.

Abstract

Some selection rules based on monotone empirical Bayes estimators of the binomial parameters are proposed. First, it is shown that, under the squared error loss, the Bayes risks of the proposed monotone empirical Bayes estimators converge to the related minimum Bayes risks with rates of convergence at least of order 0(nsub -n), where n is the number of accumulated past experiences at hand. Further, for the selection problem, the rates of convergence of the proposed selection rules are shown to be at least of order 0(exp(-cn)) for some c > 0. Keywords: Asymptotically optimal.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1986
Accession Number
ADA170093

Entities

People

  • Shanti Gupta
  • Tachen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Abstracts
  • Binomials
  • Classification
  • Computations
  • Convergence
  • Decision Theory
  • Estimators
  • Military Research
  • New York
  • Numbers
  • Observation
  • Probability
  • Random Variables
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.