On Lower Confidence for PCS in Truncated Location Parameter Models

Abstract

We are concerned with deriving lower confidence bounds for the probability of a correct selection in truncated location-parameter models. Two cases are considered according to whether the scale parameter is known or unknown. For each case, a lower confidence bound for the difference between the best and the second best is obtained. These lower confidence bounds are used to construct lower confidence bounds for the probability of a correct selection. The results are then applied to the problem of selecting the best exponential population having the largest Truncated location-parameter. Useful tables are provided for implementing the proposed methods. Keywords: Correct selection; Probability of a correct selection; Indifference zone; Lower confidence bound; Best population; Truncated-location model, Two-parameter exponential distribution.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1989
Accession Number
ADA210274

Entities

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  • Lii-yuh Leu
  • Shanti Gupta
  • Tachen Liang

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  • Purdue University

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