Empirical Bayes Rules for Selecting Good Binomial Populations. Revision.

Abstract

This paper deals with the problem of selecting good binomial populations compared with a standard or a control through the empirical Bayes approach. Two cases have been studied: one with the pior distribution completely unknown and the other with the prior distribution symmetrical about p = 1/2, but otherwise unknown. In each case, empirical Bayes rules are derived and their rates of convergence are shown to be of order O(exp(-cn)) for some c>O, where n is the number of accumulated post experiences at hand. Keywords: Statistical decision theory; Smoothing(Mathematics); Asymptotically optimal. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1985
Accession Number
ADA162867

Entities

People

  • Shanti Gupta
  • Tachen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Binomials
  • Computations
  • Convergence
  • Decision Theory
  • Estimators
  • Governments
  • Inequalities
  • Military Research
  • Probability
  • Random Variables
  • Sequences
  • Statistical Decision Theory
  • Statistical Inference
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Business Analytics
  • Statistical inference.