• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.543-550
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• 1998

From the power plant in a steel plant, environment pollutants such as $SO_x$, $NO_x$, CO and $CO_2$ are emitted by combustion reactions of the fuels which are by-product gases, oil and liquefied natural gas(LNG). To reduce the amounts of the pollutants, it is important to build a predictive model for the emission of the pollutants. In this paper, model that predict the amounts of generated pollutants for the used fuel is developed by using Gibbs free energy minimization method[1] with the temperature correction technique. For some data set, the calculation results from this model are compared with the real emission amounts of $SO_x$, $NO_x$, and the result of the calculation by both ASPEN PLUS which is a commercial simulation software. This model shows good results and can be applied to other power plants.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.263-275
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• 2014

The present study used a hierarchical Bayesian approach was used to develop a mixed effect model to describe the transitional behavior of subjects in time nonhomogeneous Markov chains. The posterior distributions of model parameters were not in analytically tractable forms; subsequently, a Gibbs sampling method was used to draw samples from full conditional posterior distributions. The proposed model was implemented with real data.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1547-1555
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• 2015

We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.589-590
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• 2005

BSCCO:2212-2223 thin films were fabricated by using the ion beam sputter with a evaporation method at various substrate temperatures, $T_{sub}$, and ozone gas pressures, $pO_3$. The correlation diagrams of the BSCCO phases with Tsub and $pO_3$ are established in the 2212 and 2223 compositional films. In spite of 2212 compositional sputtering, Bi2201 and Bi2223 as well as Bi2212 phases come out as stable phases depending on Tsub and $pO_3$. From these results, the thermodynamic evaluation of ${\Delta}H$ and ${\Delta}S$, which are related with Gibbs' free energy change for single Bi2212 or Bi2223 phase, was performed.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.441-448
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• 2000

방출단층영상 재구성을 위한 최대우도 기대값최대화(maximum likelihood expectation maximization, MLEM) 방법은 영상 획득과정을 통계학적으로 모델링하여 영상을 재구성한다. MLEM은 일반적으로 사용하여 여과후역투사(filtered backprojection)방법에 비해 많은 장점을 가지고 있으나 반복횟수 증가에 따른 발산과 재구성 시간이 오래 걸리는 단점을 가지고 있다. 이 논문에서는 이러한 단점을 보완하기 위해 계산시간을 현저히 단축시킨 배열된부분집합 기대값최대화(ordered subsets expectation maximization. OSEM)에 Gibbs 선행치인 membrance (MM) 또는 thin plate(TP)을 첨가한 OSEM-MAP (maximum a posteriori)을 구현함으로써 알고리즘의 안정성 및 재구성된 영상의 질을 향상시키고자 g나다. 실험에서 알고리즘의 수렴시간을 가속화하기 위해 투사 데이터를 16개의 부분집합으로 분할하여 반복연산을 수행하였으며, 알고리즘의 성능을 비교하기 위해 소프트웨어 모형(원숭이 뇌 자가방사선, 수학적심장흉부)을 사용한 영상재구성 결과를 제곱오차로 비교하였다. 또한 알고리즘의 사용 가능성을 평가하기 위해 물리모형을 사용하여 PET 기기로부터 획득한 실제 투사 데이터를 사용하였다.