• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.449-462
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• 2001

베이지안 다중 검정방법(multiple hypothesis test)은 여러 통계모형에서 성공적인 결과를 주는 것으로 알려져있다. 일반적으로, 베이지안 가설검정은 고려중인 모형에 대한 사후확률을 계산하여 가장 높은 확률은 갖는 모형을 선택하기 때문에 귀무가설의 기각여부에만 관심을 가지는 고전적인 분산분석 검정과는 달리 좀 더 구체적인 모형을 선택할 수 있는 장점이 있다. 이 논문에서는 독립이면서 로그정규분포를 따르는 K($\geq$3)개 모집단의 모수에 대한 가설 검정방법으로 O’Hagan(1995)이 제안한 부분 베이즈 요인을 이용한 베이지안 방법을 제안한다. 이 때 모수에 대한 사전분포로는 무정보적 사전분포를 사용한다. 제안한 검정 방법의 유용성을 알아보기 위하여 실제 자료의 분석과 모의 실험을 이용하여 고전적인 검정방법과 그 결과를 비교한다.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.1-9
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• 2004

The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.97-111
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• 1998

This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.13-26
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• 2008

Inference on the present data will be more reliable when the data arising from previous similar studies are available. The data arising from previous studies are referred as historical data. The power prior is defined by the likelihood function based on the historical data to the power $a_0$, where $0\;{\le}\;a_0\;{\le}\;1$. The power prior is a useful informative prior for Bayesian inference such as model selection and model comparison. We utilize the historical data to perform multiple comparison in the one-way ANOVA model. We demonstrate our results with some simulated datasets under a simple order restriction between the treatments.

• Genomics & Informatics
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• v.1 no.2
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• pp.80-86
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• 2003

Human Papillomavirus (HPV) infection is known as the main factor for cervical cancer which is a leading cause of cancer deaths in women worldwide. Because there are more than 100 types in HPV, it is critical to discriminate the HPVs related with cervical cancer from those not related with it. In this paper, the risk type of HPVs using their textual explanation. The important issue in this problem is to distinguish false negatives from false positives. That is, we must find high-risk HPVs as many as possible though we may miss some low-risk HPVs. For this purpose, the AdaCost, a cost-sensitive learner is adopted to consider different costs between training examples. The experimental results on the HPV sequence database show that the consideration of costs gives higher performance. The improvement in F-score is higher than that of the accuracy, which implies that the number of high-risk HPVs found is increased.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.749-754
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• 2015

This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.73-77
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• 2006

In this paper, the Bayesian procedure for the multiple test of fraction nonconforming, p, is proposed. It is the procedure for checking whether the process is out of control, in control, or under the permissible level for p. The procedure is as follows: first, setting up three types of models, $M_1:p=p_0,\;M_2:pp_0$, second, computing the posterior probability of each model. and then choosing the model with the largest posterior probability as a model most fitted for the observed sample among three competitive models. Finally, the simulation study is performed to examine the proposed method.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.909-922
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• 2014

This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.41-50
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• 2001

We propose the Bayesian testing for the equality of K-exponential populations means. Specially we use the intrinsic Bayesian factors suggested by Beregr and Perrichi (1996,1998) based on the noninformative priors for the parameters. And, we investigate the usefulness of the proposed Bayesian testing procedures via simulations.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.