On Selection Procedures for Exponential Family Distributions Based on Type-I Censored Data

Abstract

We investigate the problem of selecting the best population from exponential family distributions based on type-I censored data. A Bayes rule is derived and a monotone property of the Bayes selection rule is obtained. Following that property, we propose an early selection rule. Through this early selection rule, one can terminate the experiment on a few populations early and possibly make the final decision before the censoring time. An example is provided in the final part to illustrate the use of the early selection rule for Weibull populations.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2000
Accession Number
ADA379465

Entities

People

  • Jianjun Li
  • Shanti Gupta
  • Shuyuan He

Organizations

  • Purdue University

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  • Human Systems

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  • Mathematics

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  • Statistical inference.