• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.11-23
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• 1998

A Bayes criterion for testing the equality of covariance matrices of two multivariate normal distributions is proposed and studied. Development of the criterion invloves calculation of Bayes factor using the imaginary sample method introduced by Spiegelhalter and Smith (1982). The criterion is designed to develop a Bayesian test criterion, so that it provides an alternative test criterion to those based upon asymptotic sampling theory (such as Box's M test criterion). For the constructed criterion, numerical studies demonstrate routine application and give comparisons with the traditional test criteria.

• Journal of the Korean Statistical Society
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• 제28권1호
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• pp.107-124
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• 1999

An approximate Bayes criterion for multivariate Behrens-Fisher problem is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approach introduced by Speigelhalter and Smith (1982). The criterion is designed to develop a Bayesian test, so that it provides an alternative test to other tests based upon asymptotic sampling theory (such as the tests suggested by Bennett(1951), James(1954) and Yao(1965). For the derived criterion, numerical studies demonstrate routine application and give comparisons with the classical tests.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.193-205
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• 1999

An approximate Bayes criterion for Behrens-Fisher problem (testing equality of means of two normal populations with unequal variances) is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approachintroduced by Spiegelhalter and Smith (1982). The proposed criterion is designed to develop a Bayesian test criterion having a closed form, so that it provides an alternative test to those based upon asymptotic sampling theory (such as Welch's t test). For the suggested Bayes criterion, numerical study gives comparisons with a couple of asymptotic classical test criteria.

• 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.

• Journal of the Korean Statistical Society
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• 제26권2호
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• pp.261-276
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• 1997

In the context of binary response regression, the problem of constructing Bayesian goodness-of-link test for testing logit link versus probit link is considered. Based upon the well known facts that cdf of logistic variate .approx. cdf of $t_{8}$/.634 and, as .nu. .to. .infty., cdf of $t_{\nu}$ approximates to that of N(0,1), Bayes factor is derived as a test criterion. A synthesis of the Gibbs sampling and a marginal likelihood estimation scheme is also proposed to compute the Bayes factor. Performance of the test is investigated via Monte Carlo study. The new test is also illustrated with an empirical data example.e.

• 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.

• 응용통계연구
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• 제11권2호
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• pp.435-449
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• 1998

본 연구는 다중판별분석(MDA)에서 필요한 변수선택기준을 베이즈접근법으로 제안하였다. 이 베이즈판별변수 선택기준은 여러 정규모집단분포의 평균벡터에 대한 동질성 검정에 필요한 디폴터형태의 베이즈요인을 객관적 베이즈방법으로 유도하여 설정하였다. 디폴트베이즈요인(default Bayes factor)은 Spiegelhalter와 Smith (1982)가 계발한 가상적트레이닝표본법(imaginary training sample method)을 사용하여서 도출하였다. 또한 제안된 베이즈판별변수선택 기준이 지닌 분포의 성질을 이용하여, 추가 판별변수(또는 변수군)가 MDA에 기여하는 부가적인 판별력에 대한 검정법 및 추가판별변수(또는 변수군)의 선택 기준에 대해서도 논하였다. 본 연구에서 새로이 얻은 변수선택기준은 최적부분집합선택법(optimal subset selection method)뿐 아니라 각 단계적방법(stepwise method)의 변수선택기준으로 사용될 수 있으며, 두 그룹 판별분석에도 사용이 가능하다는 점에서 표본이론에 의해 여러 형태로 개발된 기존의 판별변수 선택 기준들을 하나로 통합시킬 수 있는 기능을 지니고 있다. 모의실험을 실시하여 최적 부분집합선택법과 단계적방법하에서 제안된 판별변수선택 기준이 가진 효용성을 평가하였다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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• pp.327-333
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• 2006

In this paper, we consider the Bayesian hypotheses testing for independence in 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 fractional Bayes factor. Also we give some numerical results to illustrate our results.

• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.123-135
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• 2000

This paper addresses the Bayesian hypotheses testing for the comparison of exponential population under type II censoring. 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, expected and median intrinsic Bayes factors for our problem. The Monte Carlo simulation is used for calculating intrinsic Bayes factors which are compared with P-values of the classical test.

• Journal of the Korean Statistical Society
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• 제28권1호
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• pp.73-92
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• 1999

In Bayesian model selection or testing problems, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion, the intrinsic Bayes factor overcomes such problems by using a part of the sample as a training sample to get a proper posterior and then use the posterior as the prior for the remaining observations to compute the Bayes factor. Surprisingly, such Bayes factor can also be computed directly from the full sample by some proper priors, namely intrinsic priors. The present paper explains how to derive intrinsic priors for simple tree ordered exponential means. Some numerical results are also provided to support theoretical results and compare with classical methods.