On Empirical Bayes Selection Rules for Negative Binomial Populations

Abstract

This paper deals with the problem of selecting good negative binomial populations as compared with a standard or a control. The main results are based on the use of the empirical Bayes approach. First the authors derive the monotone empirical Bayes estimators of the concerned parameters. Based on these estimators, they construct monotone empirical Bayes selection rules. Asymptotic optimality properties of the monotone empirical Bayes estimators and the monotone empirical Bayes selection rules are investigated. The respective convergence rates for the estimation problem and for the selection problem are studied, under some conditions.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1988
Accession Number
ADA196994

Entities

People

  • Shanti Gupta
  • Tachen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Binomials
  • Computations
  • Convergence
  • Decision Theory
  • Estimators
  • Military Research
  • New York
  • Probability
  • Random Variables
  • Standards
  • Statistical Decision Theory
  • Statistical Inference
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Statistical inference.