Bayes-P* Subset Selection Procedures for the Best Population.

Abstract

Two new selection procedures, called nonrandomized and randomized Bayes-P* procedures are defined for selecting a small nonempty subset of k populations which contains the best population. It is shown that these procedures have some optimal properties. If we restrict attention to the class D(D*) of all nonrandomized (randomized) selection procedures, which satisfy the PP*-condition, that is the posterior probability of a correct selection, for any given observation X = x, is not less than P*, a predetermined number between l/k and l, then these two new selection procedures are shown also to be Bayes decision procedures in the class D and D* respectively, provided that some regularity conditions are satisfied. Robustness of these procedures and comparisons with some other selection procedures are studied by using Monte Carlo simulations. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1984
Accession Number
ADA140179

Entities

People

  • H. M. Yang
  • Sumedha Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Data Science
  • Decision Theory
  • Distribution Functions
  • Efficiency
  • Errors
  • Information Science
  • Military Research
  • Monte Carlo Method
  • New York
  • Normal Distribution
  • Observation
  • Probability
  • Simulations
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.