• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.15-23
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• 2004

When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.141-150
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• 2000

In this paper we propose a method of computing Bayes factor to detect an outlier in a random effects model. When no information is available and hence improper noninformative priors should be used Bayes factor includes the unspecified constants and has complicated computational burden. To solve this problem we use the fractional Bayes factor (FBF) of O-Hagan(1995) and the generalized Savage0-Dickey density ratio of Verdinelli and Wasserman (1995) The proposed method is applied to outlier deterction problem We perform a simulation of the proposed approach with a simulated data set including an outlier and also analyze a real data set.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1391-1396
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• 2008

In this paper, we consider two components system which the lifetimes have Marshall and Olkin's bivariate exponential model with bivariate type I censored data. We propose a Bayesian independent test procedure for above model using fractional Bayes factor method by O'Hagan based on improper prior distributions. And we compute the fractional Bayes factor and the posterior probabilities for the hypotheses, respectively. Also we select a hypothesis which has the largest posterior probability. Finally a numerical example is given to illustrate our Bayesian testing procedure.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.297-308
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• 2010

This article deals with the problem of testing mean when the coefficient of variation in normal distribution is known. We propose Bayesian hypothesis testing procedures for the normal mean 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. Specially, we develop intrinsic priors which give asymptotically same Bayes factor with 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.17 no.4
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• pp.1329-1341
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• 2006

When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.995-1006
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• 2008

This article deals with the one-sided hypothesis testing problem in inverse Gaussian distribution. We propose Bayesian hypothesis testing procedures for the one-sided hypotheses of the shape parameter 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, the median intrinsic Bayes factor and the encompassing intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.335-349
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• 2003

In this paper, we use Bayesian method for model selection of poisson vs. negative binomial distribution, and normal, double exponential vs. cauchy distribution. The fractional Bayes factor of O'Hagan (1995) was applied to Bayesian model selection under the assumption of noninformative improper priors for all parameters in the models. Through the analyses of real data and simulation data, we examine the usefulness of the fractional Bayes factor in comparison with intrinsic Bayes factors of Berger and Pericchi (1996, 1998).

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.835-844
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• 2016

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jereys' prior or the reference prior can be used. Jereys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.927-932
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• 2006

The techniques for selecting and evaluating prior distributions are studied over recent years which the primary emphasis is on noninformative priors. But, noninformative priors are typically improper so that such priors are defined only up to arbitrary constants which affect the values of Bayes factors. In this paper, we consider the Bayesian hypotheses testing for the Weibull shape parameter based on fractional Bayes factor which is to remove the arbitrariness of improper priors. Also we present a numerical example to further illustrate our results.

• 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.