• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.233-244
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• 2006

In this paper, we develop the Bayesian multiple comparisons procedure for the binomial distribution. We suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. An example is illustrated for the proposed method. For this example, the suggested method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison. Also, some simulation was performed.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.843-850
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• 2006

In this paper, we consider the Bayesian multiple comparisons problem for K bivariate exponential populations to make inferences on the relationships among the parameters based on observations. And we suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Also, we give a numerical examples to illustrate our procedure.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.39-47
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• 2006

The Bayesian approach to multiple testing procedure for one sample testing problem proposed by Scott and Berger (2003) is extended to two-sample comparison problem in microarray experiments. The prior distribution of each gene's mean for one sample is given conditionally on the corresponding gene's mean for the other sample. Posterior distributions of interesting parameters are derived and estimated based on an importance sampling method. A simulated example is given for illustration.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1129-1140
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• 2005

A nonparametric Bayesian method for calculating posterior probabilities of the multiple comparison problem on the parameters of several Geometric populations is presented. Bayesian multiple comparisons under two different prior/ likelihood combinations was studied by Gopalan and Berry(1998) using Dirichlet process priors. In this paper, we followed the same approach to calculate posterior probabilities for various hypotheses in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships on the parameters of several geometric populations. This also leads to a simple method for obtaining pairwise comparisons of probability of successes. Gibbs sampling technique was used to evaluate the posterior probabilities of all possible hypotheses that are analytically intractable. A numerical example is given to illustrate the procedure.

• Journal of the Korean Statistical Society
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• 제29권2호
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• pp.155-168
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• 2000

Regarding to multiple comparison problem (MCP) of k normal population variances, we suggest a Bayesian method for calculating posterior probabilities for various hypotheses of equality among population variances. This leads to a simple method for obtaining pairwise comparisons of variances in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships among the variances. The method is derived from the fact that certain features of the hierarchical nonparametric family of Dirichlet process priors, in general, make it amenable to solving the MCP and estimating the posterior probabilities by means of posterior simulation, the Gibbs sampling. Two examples are illustrated for the method. For these examples, the method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.801-812
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• 1999

Change-point hazard rate models arise for example in applying "burn-in" techniques to screen defective items and in studing times until undesirable side effects occur in clinical trials. Sometimes in screening defectives it might be sensible to model two stages of burn-in. In a clinical trial there might be an initial hazard rate for a side effect which after a period of time changes to an intermediate hazard rate before settling into a long term hazard rate. In this paper we consider the multiple change points hazard rate model. The classical approach's asymptotics can be poor for the small to all moderate sample sizes often encountered in practice. We propose a Bayesian approach avoiding asymptotics to provide more reliable inference conditional only upon the data actually observed. The Bayesian models can be fitted using simulation methods. Model comparison is made using recently developed Bayesian model selection criteria. The above methodology is applied to a generated data and to a generated data and the Lawless(1982) failure times of electrical insulation.

• 한국산업정보학회논문지
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• 제7권1호
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• pp.42-49
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• 2002

이 논문에서는 정규분포를 하는 모집단에 대한 베이지안 다중비교를 개발한다. 베이지안 다중비교를 위해서는 베이즈요인의 계산이 필수적인데 베이즈 요인의 계산은 O'Hagan (1995)이 제안한 부분베이즈 요인을 이용한다. 그리고 베이지안에서 필수적인 모수에 대한 사전분포로는 무정보적 사전분포를 이용한다. 또한, 비교대상이 되는 모집단의 수가 3이상인 경우에 대하여 베이즈요인의 정확한 형태를 유도했으며 정규분포를 한다고 널리 알려져 있는 자료를 제안된 방법으로 분석하는 사례를 보였으며, 모의실험을 통하여 제안된 방법의 유용성을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.569-574
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• 2010

We consider two components system which have Freund's bivariate exponential model. In this case, Bayesian multiple comparisons procedure for failure rates is sug-gested in K Freund's bivariate exponential populations. Here we assume that the com-ponents enter the study at random over time and the analysis is carried out at some prespeci ed time. We derive fractional Bayes factor for all comparisons under non- informative priors for the parameters and calculate the posterior probabilities for all hypotheses. And we select a hypotheses which has the highest posterior probability as best model. Finally, we give a numerical examples to illustrate our procedure.

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2001년도 추계공동학술대회 논문자료집 정보화 젼략 패러다임의 변화에 대한 보기술의 대응
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• pp.155-155
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• 2001