• 응용통계연구
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• 제26권3호
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• pp.453-470
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• 2013

본 논문에서는 카드뮴의 반응-용량 모형에 대한 베이지안 분석을 실시하고 기준용량에 대한 추정값들을 유도하고 비교한다. 이를 위하여 독성물질에 대한 용량반응곡선에서 많이 활용되는 두 가지 모형을 사용하고, 카드뮴의 독성연구에 관련한 기존의 문헌으로 수집된 자료에 대한 성별, 연령, 인종, study code 등과 같은 소집단 간의 개별적 형질을 반영할 수 있는 베이지안 메타분석 관점에서의 모형분석을 실시한다. 이러한 두 가지 모형에 대한 베이지안 분석을 위하여 WinBUGS를 이용한 마르코프 연쇄 몬테칼로(Markov chain Monte Carlo; MCMC) 방법을 통하여 모수를 추정하고 이에 따른 다양한 기준용량들을 계산하고 비교해보았다. 베이지안 모형 적합뿐만 아니라 편차정보기준을 통해서 주어진 자료를 더 잘 설명하는 모형을 선택하는 베이지안 모형 선택을 고려하였고, 이를 실제 자료에 적용해본다.

• International Journal of Reliability and Applications
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• 제13권1호
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• pp.37-47
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• 2012

In this article we attempted reliability analysis of a component under the stress-strength pattern with both classical as well as Bayesian techniques. The main focus is made to develop the theory for dealing the reliability problems in various circumstances for bivariate environmental set up in context of Bayesian paradigm. A stress-strength based model describes the life of a component which has strength (Y) and is subjected to stress(X). We develop the Bayes and moment estimators of reliability of a component for each of the three possible conditions, under the assumption that the two stresses (i.e. $X_1$ and $X_2$) on a component are dependent and follow a Bivariate exponential (BVE) of Marshall-Olkin distribution, the strength of a component (Y) following exponential distribution is independent of the stresses. The simulation study is performed with Markov Chain Monte Carlo technique via Gibbs sampler to obtain the estimates of Bayes estimators of reliability, are compared with moment estimators of reliabilities on the basis of absolute biases.

• 한국전산구조공학회논문집
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• 제32권2호
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• pp.125-132
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• 2019

이 논문에서는 지진 하중을 받는 꼬인 삼각대 지지구조를 갖는 해상풍력발전기의 지진 신뢰성 해석 방법을 제시한다. 수평하중에 대해서 면외 변위가 발생하는 꼬인 삼각대지지 구조의 기하학적 특성과 지반의 비선형성을 포함한 지반-말뚝 상호작용을 고려하기 위한 구조물의 3차원 동적 유한요소 모델을 제시하였다. 지진신뢰성 평가를 위해 재현주기별 인공지진파를 사용한 시간이력 해석을 통해 말뚝 두부의 수평변위로 정의된 한계 상태식에 대하여 파괴확률을 산정하였다. 비선형 시간이력해석에 의한 한계상태식 평가를 고려하여 효율적으로 신뢰성 해석을 하기 위해 Markov Chain Monte Carlo 샘플링 방법을 적용한 부분집합 시뮬레이션 방법의 적용을 제시하였다. 제시한 방법은 2차원 모델 및 정적해석만으로는 정확한 결과를 도출할 수 없는 꼬인 삼각대 지지구조를 갖는 해상풍력발전기의 신뢰성 평가 및 설계기준 개발에 활용될 수 있음을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1645-1651
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• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.83-126
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• 2005

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. Here we propose a nonparametric estimation approach that combines wavelet methods for non-equispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• 응용통계연구
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• 제21권5호
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• pp.835-843
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• 2008

In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.225-235
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• 2009

Lattice process models are used to explain phase transitions in statistical mechanics, a branch of physics. The Ising model, a specific form of lattice process model, was proposed by Ising in 1925. Since then, variants of the Ising model such as the Potts model and the unitary cell model have been proposed. Like the Ising model, it is believed that the more general models exhibit phase transitions on the critical surface, which is based on the mathematical equation. In statistical sense, phase transitions can be simulated through Markov Chain Monte Carlo (MCMC). We applied Swendsen-Wang algorithm, a block Gibbs algorithm, to a general lattice process models and we simulate phase transition phenomena of the unitary cell model.