• 응용통계연구
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• 제16권2호
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• pp.335-349
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• 2003

본 논문에서는 포아송분포 대 음이항분포, 그리고 정규분포, 이중지 수분포 대 코쉬분포에 대한 모형선택을 위하여 베이지안 방법을 사용한다. 각 모수에 대한 사전분포로는 무정보 부적절 사전분포의 가정 하에, 베이지안 모형선택을 위하여 O'Hagan (1995)의 부분적 베 이즈요인을 이용하였다. 실제자료와 모의 실험 자료의 분석을 통하여 부분적 베이즈요인의 유용성을 Berger와 Pericchi (1996, 1998)의 내재적 베이즈요인들과 함께 비교 검토해 본다.

• Journal of the Korean Data and Information Science Society
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• 제28권1호
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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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• 제10권1호
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• pp.135-146
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• 1999

In this paper, we consider the Bayesian hypotheses testing for independence and symmetry in Freund's bivariate exponential model. In Bayesian testing problem, we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and use these results to analyze some data sets.

• 품질경영학회지
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• 제28권3호
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• pp.1-10
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• 2000

In this paper, we address the Bayesian hypotheses testing for the comparison of Weibull distributions. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are Improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors for the comparison of Weibull lifetime model and we use these results to analyze real data sets.

• 품질경영학회지
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• 제27권4호
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• pp.153-166
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• 1999

In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.151-164
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• 2000

In this paper we consider the Bayesian hypothese testing for independence and symmetry in Freund's bivariate exponential model with censored data In Bayesian testing problem we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and use these results of analyze some data sets.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.551-566
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• 2006

In this paper, we consider a Bayesian model selection for the intraclass correlation coefficient in familiar data. In particular, we compare two nested models such as the independence and intraclass models using the reference prior. A criterion for testing is the Bayesian Reference Criterion by Bernardo (1999) and the Intrinsic Bayes Factor by Berger and Pericchi (1996). We provide numerical examples using simulation data sets for illustration.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.147-152
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• 2000

A Bayesian criterion is proposed for a multiple test of two independent multivariate normal populations. For a Bayesian test the fractional Bayes facto.(FBF) of O'Hagan(1995) is used under the assumption of Jeffreys priors, noninformative improper proirs. In this test the FBF without the need of sampling minimal training samples is much simpler to use than the intrinsic Bayes facotr(IBF) of Berger and Pericchi(1996). Finally, a simulation study is performed to show the behaviors of the FBF.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.541-548
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• 2000

Various statistical methods for assessment of equivalence in average bioavailabilities have been developed under the assumption that the intra-subject variabilities for the test and reference formulations are the same. Without the assumption, assessing the equivalence in average bioavailabilites does not imply that the two formulations are therapeutically equivalent and exchangeable. The most commonly used test procedure for equality of variabilites in 2$\times$2 crossover experiment is the so called Pitman-Morgan's adjusted F test based on the model without carryover effects (Chow and Liu(1992)). In this paper, a Bayesian method based on the Intrinsic Bayes Factor is proposed, which can be applied to the model with carryover effects.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.97-107
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• 2001

A simultaneous test criterion for multiple hypotheses concerning comparison of two multivariate normal populations is considered by using the so called Bayes factor method. Fully parametric frequentist approach for the test is not available and thus Bayesian criterion is pursued using a Bayes factor that eliminates its arbitrariness problem induced by improper priors. Specifically, the fractional Bayes factor (FBF) by O'Hagan (1995) is used to derive the criterion. Necessary theories involved in the derivation an computation of the criterion are provided. Finally, an illustrative simulation study is given to show the properties of the criterion.