Isotonic Procedures for Selecting Populations Better than a Control Under Ordering Prior

Abstract

The problem of selecting a subset containing all populations better than a control under an ordering prior is considered. Three new selections procedures which satisfy a desirable basic requirement on the probability of a correct selection are proposed and studied. Two of the three procedures use the isotonic regression over the sample means of the k-treatments with respect to the given ordering prior. Tables of constants which are necessary to carry out the selection procedures with isotonic approach for the selection of unknown means of normal populations are given. The results including Monte Carlo studies indicate that, in general, the stepwise procedure delta sub 1, using isotonic estimators, is the best. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA109288

Entities

People

  • Hwa-ming Yang
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Data Analysis
  • Distribution Functions
  • Equations
  • Estimators
  • Inequalities
  • Information Science
  • Military Research
  • New York
  • Probability
  • Random Variables
  • Random Walk
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.