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Abstract Details
Activity Number:
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26
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #306095 |
Title:
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Bayesian Planning and Inference of a Progressively Censored Sample from Linear Hazard Rate Distribution
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Author(s):
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Ananda Sen*+
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Companies:
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University of Michigan
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Address:
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1018 Fuller Street, Ann Arbor, MI, 48104, United States
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Keywords:
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Linear hazard rate ;
Progressive Censoring ;
Bayesian Inference ;
Bayesian Planning
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Abstract:
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In reliability and life testing, linear hazard rate (LHR) distributions are useful in modeling the life length of a system or component when failures occur at random as well as from aging or wear-out. This two parameter model in the increasing failure rate class is a special case of the polynomial hazard function models and constitutes a generalization of the exponential distribution in a direction distinct from the gamma and Weibull. Motivation of LHR and its applications in engineering and biomedical context have been demonstrated aptly in the literature. In this talk we propose a Bayesian framework unifying Type I and Type II progressive censoring schemes. Under independent gamma prior for the parameters, the joint posterior is obtained as a finite mixture that makes simulation based computations for estimation and prediction easy. A joint credible set is proposed that utilizes the posterior distribution of certain quantities in an efficient way. Bayesian planning strategies are explored through extensive numerical computation that searches for the optimal progressive censoring schemes under a variance criterion and a criterion based on the credible interval for percentiles.
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