• Journal of the Korean Data and Information Science Society
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• 제25권2호
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• pp.465-472
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• 2014

This article deals with the problem of testing the equality of the scale parameters in the half logistic distributions. We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative priors. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be dened up to a multiplicative constant. Thus 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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• 제24권4호
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• pp.949-957
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• 2013

In this paper, we consider the problem of testing the equality of the scale parameters in two parameter exponential distributions. We propose Bayesian testing procedures for the equality of the scale parameters under the noninformative priors. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. Thus, 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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• 제15권3호
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• pp.677-687
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• 2004

We consider the comparison of two one-parameter exponential distributions with the complete data as well as the type II censored data. We adapt Bayesian test procedure for nested hypothesis based on the Bayesian reference criterion. Specifically we derive the expression for the Bayesian reference criterion to solve our problem. Also we provide numerical examples using simulated data sets to illustrate our results.

• Journal of the Korean Statistical Society
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• 제28권4호
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• pp.503-513
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• 1999

The Bayes factor for the identification of stationary ARM(p,q) models is exactly computed using the Monte Carlo method. As priors are used the uniform prior for (\ulcorner,\ulcorner) in its stationarity-invertibility region, the Jefferys prior and the reference prior that are noninformative improper for ($\mu$,$\sigma$\ulcorner).

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.645-654
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• 2004

In this paper, we develop the Bayesian test procedure for the intraclass correlation coefficient in the unbalanced one-way random effect model based on the reference priors. That is, the objective is to compare two nested model such as the independent and intraclass models using the factional Bayes factor. Thus the model comparison problem in this case amounts to testing the hypotheses $H_1:\rho=0$ versus $H_2:{\rho}{\neq}0$. Some real data examples are provided.

• 응용통계연구
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• 제17권1호
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• pp.49-60
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• 2004

이 논문의 목적은 역 가우스 분포의 모수비가 관심의 대상일 때, 그 모수비에 대한 무정보적 사전분포를 구하는데 있다. 특별히, 모수비에 대한 확률대응사전분포와 기준 사전분포를 제안하였다. 먼저, 관심의 대상이 되는 모수에 대해 모수 직교화 변환을 구하고, 모수 직교화 변환을 이용하여 확률대응사전분포와 기준사전분포를 구하였다. 특히 확률대응사전분포의 일치차수는 1차임을 보였으며 2차 확률대응사전분포는 존재하지 않음을 보였다. 또한 제안된 사전분포에 의해 유도된 사후분포는 적절 분포임을 증명하였다. 모의 실험을 통하여 확률대응사전분포와 기준사전분포를 비교했으며, 실제자료를 이용하여 분석하는 예를 보였다.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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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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• 제21권2호
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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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• 제16권3호
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• pp.539-547
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• 2005

We propose the Bayesian testing for the equality of two independent Inverse Gaussian population means using the fractional Bayesian factors suggested by O' Hagan(1995). As prior distribution for the parameters, we assumed the noninformative priors. In order to investigate the usefulness of the proposed Bayesian testing procedures, the behaviors of the proposed results are examined via real data analysis.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1119-1128
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• 2005

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor and the fractional Bayes factor have been used to overcome this problem. In this paper, we suggest a Bayesian hypothesis testing based on the intrinsic Bayes factor and the fractional Bayes factor for the comparison of two lognormal variances. Using the proposed two Bayes factors, we demonstrate our results with some examples.