Empirical Bayes Selection Procedures for Selecting the Best Logistic Population Compared with a Control

Abstract

In this paper we investigate the problem of selecting the best logistic population from k(greater than or equal 2) possible candidates. The selected population must also be better than a given control. We employ the empirical Bayes approach and develop a selection procedure. The performance (rate of convergence) of the proposed selection rule is also analyzed. We also carry out a simulation study to investigate the rate of convergence of the proposed empirical Bayes selection procedure. The results of simulation are provided in the paper.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1998
Accession Number
ADA358196

Entities

People

  • Lin Xun
  • Lin Zhengyan
  • Shanti Gupta

Organizations

  • Purdue University

Tags

DTIC Thesaurus Topics

  • Convergence
  • Data Science
  • Decision Theory
  • Distribution Functions
  • Experimental Design
  • Information Science
  • Military Research
  • New York
  • Normal Distribution
  • Observation
  • Order Statistics
  • Probability
  • Random Variables
  • Simulations
  • Standards
  • Statistical Decision Theory
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.