Bayesian Hypothesis Testing for Homogeneity of the Shape Parameters in the Gamma Populations
Sang-Gil Kang, Dal-Ho Kim, Woo-Dong Lee
Abstract
Sang-Gil Kang, Dal-Ho Kim, Woo-Dong Lee
Abstract
In this paper, we consider the hypothesis testing for the homogeneity of the shape parameters in the gamma distributions. The noninformative priors such as Jeffreys# prior or reference prior are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian testing procedure for the homogeneity of the shape parameters based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.
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In this paper, we consider the hypothesis testing for the homogeneity of the shape parameters in the gamma distributions. The noninformative priors such as Jeffreys# prior or reference prior are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian testing procedure for the homogeneity of the shape parameters based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.
Key concepts: Bayes factor, Homogeneity (statistics), Prior probability, Bayesian probability, Mathematics, Bayes' theorem, Multiplicative function, Statistics