On Selecting the Best Among Good Populations Based on a Two-Stage Procedure: A Bayesian Approach with Applications.

Abstract

Several procedures have been studied to select the best among a set of new treatments (populations) which are better than a standard (or control) using two-stage procedures for the case of normal populations. One such procedure is to select the best based on the confidence intervals with a specified fixed width 2d after eliminating those populations which are worse than the standard based on the expected posterior losses. Several papers deal with this kind of problem but none of them is based on the so-called 100(1-2 alpha)% Highest Posterior Density (HPD) credible regions, which are conceptually equivalent to the confidence intervals, with a fixed width 2d. After retaining good populations based on the expected posterior losses, we set up a stopping rule Ni for constructing the HPD credible region for each selected population, which is asymptotically efficient and consistent. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1983
Accession Number
ADA129446

Entities

People

  • Joong K. Sohn
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Networks
  • Decision Theory
  • Design Criteria
  • Elimination
  • Intervals
  • Military Research
  • New York
  • North Carolina
  • Operations Research
  • Probability
  • Probability Density Functions
  • Random Variables
  • Standards
  • Statistical Decision Theory
  • Statistics
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms