| Summary: | 博士 === 國立中央大學 === 統計研究所 === 97 === In Bayesian approach to model selection or hypothesis, it is typically not possible to utilize standard noninformative distributions. In this thesis, we apply two objective Bayesian model selection methods, namely the intrinsic Bayes factor (Berger and Pericchi (1996c)) and the expected posterior Bayes factor (Péerez and Berger (2002)), to the problem of selecting models among exponential distributions. Both methods can deduce objective priors, called intrinsic prior and expected posterior prior, respectively, that can be used as a "default" prior directly to all the statistical problems encountered in the same scope.
We also consider that the parameter space has umbrella structure and utilize the expected posterior Bayes factors to decide the peak position. We will discuss the problem by using the expected posterior Bayes factor approach when the data are from k exponential distributions with different means. Simulation results show that the proposed algorithms provide accurate results.
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