Empirical Bayes Selection for the Highest Probability of Success in Negative Binomial Distributions

Abstract

The author studies the problem of selecting the highest probability of success from among several negative binomial distributions via the nonparametric empirical Bayes approach. A monotone selection rule is proposed on basis of monotone empirical Bayes estimators of the negative binomial success probabilities which are obtained by using the antitonic and isotonic regression methods. The asymptotic optimality property of the proposed empirical Bayes selection rule is also established.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1989
Accession Number
ADA210273

Entities

People

  • Tachen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Binomials
  • Computations
  • Data Science
  • Decision Theory
  • Estimators
  • Information Science
  • Mathematics
  • Military Research
  • Numbers
  • Probability
  • Sequences
  • Statistical Algorithms
  • Statistical Decision Theory
  • Statistical Inference
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.