• 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.

• 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.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 춘계 학술발표회 논문집
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• pp.67-72
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• 2002

A Bayesian testing procedure is proposed for assessment of bioequivalence in both mean and variance which ensures population bioequivalence under normality assumption. We derive the joint posterior distribution of the means and variances in a standard 2 ${\times}$ 2 crossover experimental design and propose a Bayesian testing procedure for bioequivalence based on a Markov chain Monte Carlo methods. The proposed method is applied to a real data set.

• Journal of the Korean Data and Information Science Society
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• 제20권6호
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• pp.1191-1197
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• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.521-527
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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.

• 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.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.81-94
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• 1996

This paper suggests a new criterion for testing the general linear hypothesis about coefficients in multivariate growth curve model. It is developed from a Bayesian point of view using the highest posterior density region methodology. Likelihood ratio test criterion(LRTC) by Khatri(1966) results as an approximate special case. It is shown that under the simple case of vague prior distribution for the multivariate normal parameters a LRTC-like criterion results; but the degrees of freedom are lower, so the suggested test criterion yields more conservative test than is warranted by the classical LRTC, a result analogous to that of Berger and Sellke(1987). Moreover, more general(non-vague) prior distributions will generate a richer class of tests than were previously available.

• Communications for Statistical Applications and Methods
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• 제15권4호
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• pp.531-542
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• 2008

시계열 자료를 분석할 때, 시계열 자료가 가지고 있는 추세를 제거하기 위하여 결정적 추세인 경우 회귀모형을 이용하고, 확률적 추세인 경우 차분하는 방법을 이용한다. 이 때 제거의 옳바른 기준이 되는 검정 방법이 단위근 검정이다. 그러나 기존의 Dickey-Fuller 검정 (Dickey와 Fuller, 1979)은 표본 수가 작고, 단위근에 가까울 경우 검정력이 낮으며, 베이지안 단위근 검정은 절차가 복잡하다. 본 논문에서는 기존의 두 방법들의 문제점을 해결하기 위하여, 전통적 Dickey-Fuller 검정 방법과 베이지안 방법을 결합한 형태의 검정방법으로 제안하였다. 제안된 검정방법은 모형 AR(1)에서 계수가 거의 1이거나 표본 수가 작을 경우, 기존의 Dickey-Fuller 검정보다 검정력이 높을 뿐만 아니라 일반적인 베이지안 방법 보다 절차가 간단한 검정법이 된다.

• Smart Structures and Systems
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• 제17권3호
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• 한국군사과학기술학회지
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• 제21권1호
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• pp.124-132
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• 2018

This paper presents a shelf life assessment for K-1 military gas masks in the Republic of Korea using test data of Chemical Materiels Stockpile Reliability Program(CSRP). For the shelf life assessment, over 2,500 samples between 2006 and 2015 were collected from field tests and analyzed to estimate a probability of proper and improper functionality using Bayesian estimation. For this, three stages were considered; a pre-processing, a processing and an assessment. In the pre-processing, major components which directly influence the shelf life of the mask were statistically analyzed and selected by applying principal component analysis from all test components. In the processing, with the major components chosen in the previous stage, both proper and improper probability of gas masks were computed by applying Bayesian estimation. In the assessment, the probability model of the mask shelf life was analyzed with respect to storage periods between 0 and 29 years resulting in between 66.1 % and 100 % performances in accuracy, sensitivity, positive predictive value, and negative predictive value.