• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.621-634
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• 2008

This paper addresses the problem of testing whether the means in several inverse Gaussian populations with heterogeneity are equal. The analysis of reciprocals for the equality of inverse Gaussian means needs the assumption of equal scale parameters. We propose Bayesian model selection procedures for testing equality of the inverse Gaussian means under the noninformative prior without the assumption of equal scale parameters. The noninformative prior is 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 model selection procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and real data analysis are provided.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1191-1203
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• 2007

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.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.217-225
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• 2017

This article deals with the problem of multiple hypothesis testing for the shape parameter in the generalized exponential distribution. We propose Bayesian hypothesis testing procedures for multiple hypotheses of the shape parameter with the noninformative prior. The Bayes factor with the noninformative prior is not well defined. The reason is that the most of the noninformative prior can be improper. Therefore we study the default Bayesian multiple hypothesis testing methods using the fractional and intrinsic Bayes factors with the reference priors. Simulation study is performed and an example is given.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1477-1489
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• 2008

This article deals with the problem of testing whether the effect size in paired study exists. We propose Bayesian hypothesis testing procedures for the effect size in paired study under the noninformative prior. The noninformative prior is 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 hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.269-277
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• 2000

We propose the Bayesian testing for the equality of two log-normal population means. Specifically we use the intrinsic Bayes factors suggested by Berger and Perichi (1996, 1998) based on the noninformative priors for the parameters. In order to investigate the usefulness of the proposed Bayesian testing procedures, we compare it with classical tests via both real data analysis and simulation.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1583-1592
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• 2015

This paper deals with the problem of testing on the shape parameter in the log-logistic distribution. We propose default Bayesian testing procedures for the shape parameter 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. We can solve the this problem by the intrinsic Bayes factor and the fractional Bayes factor. 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.961-970
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• 2014

This article deals with the problem of testing the equality of the scale parameters of several inverted exponential distributions. We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1299-1308
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• 2012

This article deals with the problem of testing the equality of the scale parameters in nonregular Pareto distributions.We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be de ned up to a multiplicative constant. So we propose the default Bayesia hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.605-616
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• 2012

This article deals with the problem of testing on the common mean of several normal populations. We propose Bayesian hypothesis testing procedures for the common normal mean under the noninformative prior. The noninformative prior is usually improper and yields a calibration problem that makes the Bayes factor to be defined u to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1569-1579
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• 2014

This article deals with the problem of testing for the equality of the shape parameters in two inverse Weibull distributions. We propose Bayesian hypothesis testing procedures for the equality of the shape parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.