• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.719-729
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• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.63-75
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• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.99-118
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• 2021

In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

• Nuclear Engineering and Technology
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• 제53권2호
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• pp.357-367
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• 2021

In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

• 자원ㆍ환경경제연구
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• 제19권2호
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• pp.215-242
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• 2010

본 연구는 유가충격 및 에너지절감유도정책충격의 효과를 고려할 수 있는 베이지안 동태확률일반균형모형의 구축을 통해 이들 외생적 충격들의 역할과 함께 현재 우리나라에서 진행 중인 고효율 에너지 소비구조로의 전환이 유발시키는 경제적 효과에 관해 분석하였다. 분석 결과에 의하면 유가상승충격은 소비, 고용, 투자 및 산출량에 부정적 효과를 주었으며, 자본 1단위당 에너지 소비량 절감을 유발시키는 에너지절감유인정책충격은 에너지 소비량의 절감과 함께 소비, 고용, 투자 및 산출량에 긍정적 효과를 주는 것으로 나타났다. 특히, 분산분해 결과는 유가충격이 설비가동률, 에너지 소비량 및 산출량의 단기적 총 변동에 가장 큰 영향을 미치며, 에너지절약정책충격 역시 기술충격과 유가충격에 비해 그 크기와 지속성 면에서 미미한 수준이나, 이들 변수들의 단기적 총 변동을 일정부분 설명할 수 있는 것으로 나타났다. 마지막으로 석유소비의 비중 축소 및 에너지 소비의 효율성 증대는 유가충격이 주는 부정적 효과를 약화시켜, 이와 같은 에너지 소비구조의 전환이 유가변동의 충격을 축소시키고 있다는 최근의 논의들과 부합됨을 확인할 수 있었다.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 경영과학
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• 제34권2호
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• pp.15-33
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• 2017

This paper considers a Bayesian Network analysis for understanding the heterogeneous cross-category effects of different promotion activities and developing an efficient overall promotion strategy for a large retail store. More specifically we differentiate price reduction promotion and floor promotion and study their heterogeneous effect on consumer purchase behavior under a market basket setting. We then utilize Bayesian networks in identifying complex association structure in market basket dataset by analyzing the effects of different promotional activities and also include the effects of time, family income and size. We find from our Bayesian network analysis that the dominant cross-category promotion effect of price promotion is the indirect effect whereas the dominant cross-category promotion effect of floor promotion is the direct effect. Also, among the demographic variables we find that family size of the household is linked with more product categories compared to income and see that there are differences in the extent of the effects by product category. Finally, we also show the existence of products acting as a network hub and how they can be utilized by retailers faced with a limited marketing budget and suggest a more efficient promotion strategy.

• 응용통계연구
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• 제15권1호
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• pp.139-151
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• 2002

본 논문에서는 베이지안 기법을 이용한 비선형회귀모형의 선택법을 제안하였다. 베이즈요인에 기초한 이 방법은 주로 대표본의 경우에 이용되는 고전적 모형선택법에 비해 사전정보를 이용하는 측면과 비내포모형 및 소표본의 경우에 대해서도 효과적으로 사용될 수 있다는 장점을 가진다. 본 논문에서는 정보적 사전분포를 고려하였으며, 베이즈요인의 추정 방법으로 Laplace - Metropolis 추정 법을 제안하였다. 또한 MCMC 과정을 통해 추정된 모수의 수렴진단에 대해서도 고려하였다. 실제자료에 대한 최적의 모형선택 및 진단과정을 구체적으로 제시하였다.

• 통합자연과학논문집
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• 제4권2호
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• pp.121-129
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• 2011

The purpose of this paper is to estimate the fluctuation of an earning rate and risk management using the price index of Korea stocks. After an observation of conception of fluctuation, we can show volatility clustering and fluctuation phenomenon in the Korea stock price index using GARCH model with heteroscedasticity. In addition, the effects of fluctuation on the time-series was evaluated, which showed the heteroscedasticity. MCMC method and Winbugs as Bayesian computation were used for analysis.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.466-466
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• 2015

지역빈도해석은 수문학에서 오랜 역사를 갖고 있으며, 수년에 걸쳐 수문학적 변량의 정량적 추정을 위해 다양한 접근방법들이 제안되어 왔다. 그러나 제안된 방법들의 가설설정 수준이 높기 때문에 실제 적용에 제약이 많고, 적용 시에도 예측에 대한 불확실성이 높은 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하기 위한 방법으로 계층적 베이지안 모델을 이용한 지역빈도해석 모형을 제안하고자 한다. 본 모형은 2개의 계층적 구조로 구성된다. 첫번째 계층은 재현기간별 GEV 분포의 매개변수를 정규화하여 주변분포로 설정하고, Kriging 기법을 이용하여 지형학적, 기상학적 정보들과 극치강수량 효과를 적합시켜 공간적 이질성과 미계측 유역에 대한 효과적인 보간을 가능하게 한다. 두번째 계층은 지점의 특성을 나타내는 매개변수들간의 공분산을 Bayesian 모델에 연계하여 매개변수들의 공간적 변동성을 나타낸다. 2개 계층의 결합확률분포는 MCMC 기법을 이용하여 예측값에 대한 불확실성을 정량적으로 분석하게 된다. 본 모형을 통해 홍수량 추정 시 필요한 시간 단위 극치강수량의 공간적 분포를 효과적으로 추정할 수 있을 것으로 판단된다.