Default Bayesian testing equality of scale parameters in several inverse Gaussian distributions
Sang Gil Kang, Dal Ho Kim, Woo Dong Lee
Abstract
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Sang Gil Kang, Dal Ho Kim, Woo Dong Lee
Abstract
Open-access reader
This paper deals with the problem of testing about the equality of the scale parameters in several inverse Gaussian distributions. We propose default Bayesian testing procedures for the equality of the shape parameters under the reference priors. The reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. Therefore we propose the default Bayesian testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.
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This paper deals with the problem of testing about the equality of the scale parameters in several inverse Gaussian distributions. We propose default Bayesian testing procedures for the equality of the shape parameters under the reference priors. The reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. Therefore we propose the default Bayesian testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.
Key concepts: Prior probability, Bayes factor, Bayesian probability, Mathematics, Multiplicative function, Bayes' theorem, Gaussian, Scale (ratio)