Hypotheses Testing for the Shape Parameter of the Weibull Lifetime Data
Sang-Gil Kang, Dal-Ho Kim, Jang-Sik Cho
Abstract
Sang-Gil Kang, Dal-Ho Kim, Jang-Sik Cho
Abstract
In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.
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In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.
Key concepts: Bayes factor, Weibull distribution, Bayes' theorem, Prior probability, Bayesian probability, Shape parameter, Mathematics, Statistics