• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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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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• v.24 no.4
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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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• v.22 no.6
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• pp.1265-1273
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• 2011

This article deals with the problem of testing the equality of the location parameters in the half-normal distributions. We propose Bayesian hypothesis testing procedures for the equality of the location parameters under the noninformative prior. The non-informative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to arbitrary constants. This problem can be deal with the use of the fractional Bayes factor or intrinsic Bayes factor. 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.

• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.115-131
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• 2000

이 논문에서는 평균-이동모형(mean-shift model)을 이상점을 위한 대립모형으로 사용하여 변량모형(random effect model)에서의 이상점 검출을 위한 베이즈인자(Bayes factor)를 제시한다. 그러나 가능한 사전 정보가 없어서 무정보사전분포(noninformative prior distribution)가 사용되어야만 할 때, 대부분의 무정보사전분포는 부적절분포(improper distribution)이기 때문에 베이즌 인자에는 사전분포로부터 나온 미지의 상수가 포함되어 잇다. 이 문제를 해결하기 위해 이 논문에서는 Berger와 Pericchi (1996)가 제시한 내재베이즈인자(the intrinsic Bayes factor;IBF)를 사용한다. 또한 이 베이즈인자를 계산상 어려움을 해결하기 위해 Verdinellidh Wasserman(1995)의 일반화 세비디지키 밀도비를 이용하여 수정하고 이것을 이용하여 이상점을 검출하는 방법을 제시한다. 마지막으로 인위적으로 이상점을 포함하고 있는 데이터를 만들고 제시된 방법으로 가상실험을 하고 또한 실제 데이터에서 제시한 방법으로 이상점을 찾아보았다.