Abstract Details
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Activity Number:
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565
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 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 #311801
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Title:
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Bayesian Model Selection Under Progressive Type-I Interval Censoring
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Author(s):
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Yu-Jau Lin*+ and Yuhlong Lio
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Companies:
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Chung Yuan Christian University and University of South Dakota
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Keywords:
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MLE ;
Metropolish-Hastings Algorithm ;
Mixture model ;
MCMC ;
Censoring ;
Bayesian statistics
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Abstract:
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Bayesian estimations for population parameters under progressive type-I interval censoring are studied via Markov Chain Monte Carol (MCMC) simulation. Two competitive statistical models, generalized exponential and Weibull distributions for modelling a real data set which contains 112 patients with plasma cell myeloma, are studied for illustration. In the model selection, a novel Bayesian procedure which involves a mixture model is proposed. Then the mix proportion is estimated through the MCMC and used as the model selection criterion.
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Authors who are presenting talks have a * after their name.
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