• 응용통계연구
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• 제6권1호
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• pp.173-181
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• 1993

각 성분 문제에서, 표본의 크기가 상이한 경우 Dirichlet process prior에 대한 경험적 베이 즈에 대한 분포함수의 추정문제를 연구하였다. 특히, 위의 경험적 베이즈 문제에 사용할 수 있도록 Zehnwirth의 $\alpha(R)$을 수정하였다.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.553-560
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• 2007

In this paper, we consider two components system which lifetimes have Freund's bivariate exponential model with equal failure rates. We propose Bayesian multiple comparisons procedure for the failure rates of I Freund's bivariate exponential populations based on Dirichlet process priors(DPP). The family of DPP is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo(MCMC) method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of multiple comparisons problem for the failure rates of bivariate exponential populations is illustrated through a numerical example.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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• pp.71-80
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• 2006

A nonparametric Bayesian multiple comparisons problem (MCP) for dependence parameters in I bivariate exponential populations is studied here. A simple method for pairwise comparisons of these parameters is also suggested. Here we extend the methodology studied by Gopalan and Berry (1998) using Dirichlet process priors. The family of Dirichlet process priors is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of MCP for the dependent parameters of bivariate exponential populations is illustrated through a numerical example.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.39-45
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• 2003

Ghosh and Ramamoorthi (1996) studied the posterior consistency for survival models and showed that the posterior was consistent, when the prior on the distribution of survival times was the Dirichlet process prior. In this paper, we study the posterior consistency of survival models with neutral to the right process priors which include Dirichlet process priors. A set of sufficient conditions for the posterior consistency with neutral to the right process priors are given. Interestingly, not all the neutral to the right process priors have consistent posteriors, but most of the popular priors such as Dirichlet processes, beta processes and gamma processes have consistent posteriors. For extended beta processes, a necessary and sufficient condition for the consistency is also established.

• 응용통계연구
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• 제14권1호
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• pp.161-175
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• 2001

메타분석(Meta-analysis)은 서로 독립적으로 연구되어진 결과들을 전체적인 하나의 결과로 도출하기 위해 사용되어지는 통계적 방법이다. 이러한 통계적 방법을 설명할 모형으로는 선택모형(selection model)을 포함한 계층적 모형(hierarchical model)을 사용하며, 이러한 모형들은 베이지안 메타분석에 유용한 것으로 알려져 있다. 그러나, 메타분석의 자료들은 일반적으로 출판편의(publication bias)를 갖고 있으므로 이를 극복하고자 가중함수(weight function)를 이용하여 분포함수를 새롭게 정의하여 사용한다. 최근에 Silliman(1997)은 계층적 모형(hierarchical model)에 가중함수를 첨부한 계층적 선택모형(hierarchical selection model)을 정의하고 모수적 베이지안 방법을 제시하였다. 본 연구에서는 미관측된 연구효과에 디리슈레 과정 사전분포(Dirichlet process prior)를 적용한 준모수적 계층적 선택모형(semiparametric hierarchical selection models)을 소개한다. 여기서 제시된 준모수적 계층적 선택모형을 베이지안 방법으로 추정하기 위하여 마코프 연쇄 몬테칼로(Markov chain Monte Carlo)방법을 이용한다. 제시된 방법을 적용하기 위하여 실제 자료(Johnson, 1993)인 충치를 예방하기 위한 두 가지의 예방약의 효과에 대한 차이를 비교하기 위해 얻어진 12개의 연구를 이용하여 메타분석을 한다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.111-119
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• 2007

In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.1-16
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• 2002

Since changepoint identification is important in many data analysis problem, we wish to make inference about the locations of one or more changepoints of the sequence. We consider the Bayesian nonparameteric inference for multiple changepoint problem using a Bayesian segmentation procedure proposed by Yang and Kuo (2000). A mixture of products of Dirichlet process is used as a prior distribution. To decide whether there exists a single change or not, our approach depends on nonparametric Bayesian Schwartz information criterion at each step. We discuss how to choose the precision parameter (total mass parameter) in nonparametric setting and show that the discreteness of the Dirichlet process prior can ha17e a large effect on the nonparametric Bayesian Schwartz information criterion and leads to conclusions that are very different results from reasonable parametric model. One example is proposed to show this effect.

• 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.