A Lower Confidence Bound on the Probability of a Correct Selection.

Abstract

In the problem of selecting the best of k populations, a natural rule is to select the population corresponding to the largest sample value of an appropriate statistic. As a retrospective analysis, a lower confidence bound on the probability of a correct selection is derived when the probability density function has the monotone likelihood ratio property under the location parameter setting. The result is applied to the normal populations with both known and unknown common variance. Tables to implement the confidence bound are provided. Additional keywords: Charts; Tables(Data). (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA159164

Entities

People

  • W. C. Kim

Organizations

  • Purdue University

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Communities of Interest

  • Materials and Manufacturing Processes

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  • Abstracts
  • Computing-Related Activities
  • Data Science
  • Decision Theory
  • Estimators
  • Information Science
  • Military Research
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  • Normal Distribution
  • Probability
  • Statistical Analysis
  • Statistical Decision Theory
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  • United States Government
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Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.