Selection of the Best with a Preliminary Test for Location-Scale Models

Abstract

This paper deals with the problem of selecting the best population from among k(> or = 2) populations which are location-scale models. New selection procedures are proposed for selecting the unique best in terms of the largest location parameter. The procedures include preliminary tests which allow the experimenter to have an option to not select if the statistical evidence is not significant. Two probabilities, the probability to make a selection and the probability of a correct selection, are controlled by these selection procedures. Applications to the normal mean models are considered. Comparisons between the proposed selection procedures and certain earlier existing procedures are also made. Finally, a two-stage procedure for the normal means problem is considered.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1989
Accession Number
ADA211583

Entities

People

  • Lii-yuh Leu
  • Shanti Gupta
  • Tachen Liang

Organizations

  • Purdue University

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  • Counter IED
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  • Abstracts
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  • Mathematics

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  • Regression Analysis.