• 천문학논총
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• 제30권2호
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• pp.315-319
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• 2015

We consider a late decaying dark matter model in which cold dark matter begins to decay into relativistic particles at a recent epoch ($z{\leqslant}1$). A complete set of Boltzmann equations for dark matter and other relevant particles particles is derived, which is necessary to calculate the evolution of the energy density and density perturbations. We show that the large entropy production and associated bulk viscosity from such decays leads to a recently accelerating cosmology consistent with observations. We determine the constraints on the decaying dark matter model with bulk viscosity by using a MCMC method combined with observational data of the CMB and type Ia supernovae.

• 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.

• Journal of applied mathematics & informatics
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• 제41권2호
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• pp.449-468
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• 2023

This paper deals in the study of the estimation of the parameters of Erlang distribution based on rank set sampling and some of its modifications. Here we considered Maximum Likelihood (ML) and the Bayesian technique to estimate the shape and scale parameter of Erlang distribution based on RSS and its some modifications such as ERSS, MRSS, and MRSSu. The derivation for unknown parameters of Erlang distribution is well presented using normal approximation to the asymptotic distribution of ML estimators. But due to the complexity involves in the integral, the Bayes estimator of unknown parameters is obtained using MCMC method. Further, we compared the MSE of estimation in different sampling schemes with different set sizes and cycle size. A real-life data application is also given to illustrate the efficiency of the proposed scheme.

• 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.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.31-40
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• 2009

공간시계열모형은 공간의 위치와 시간의 흐름에 따라 동시에 관측되는 분야인 기상, 지질, 천문, 생태, 역학 등에서 넓이 사용되고 있는 매우 복잡한 모형이다. 본 논문은 공간시계열모형에 대한 모수 추정에 있어서 기존의 최대우도추정 방법이 가지는 컴퓨팅의 문제를 해결하기 위하여 모수에 대한 사전정보와 자료의 정보를 모두 이용하는 깁스샘플링과 같은 MCMC 방법으로 모수를 추정하고, 실제 적용사례분석으로 여러 가지 측도를 구해서 추정된 모수에 대한 수렴진단을 수행하였다.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1133-1146
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• 2016

공간통계 방법 중 지역에 대한 어떤 집합체 자료나 평균자료들을 분석하는데 일반적으로 공간적 자기회귀 (conditionally autoregressive) 모형을 사용한다. 공간적 자기회귀 모형에 정의되는 공간적 이웃 소지역들은 중점의 거리나 근접성으로 정의된다. Kyung과 Ghosh (2009)는 방향에 따라서 이웃간 자기상관성의 크기가 다른 확장된 공간 모형을 제시하였다. 제안된 방향적 조건부 자기회귀 (directional conditionally autoregressive) 모형은 고유 이방성을 모형화하여 기존의 CAR과정을 일반화한다. 제시한 방향적 조건부 자기회귀모형의 모수추정으로 마르코프 체인 몬테 카를로 방법을 기반으로 한 베이즈 추정법을 제시한다. 제시한 모형을 스코틀랜드 그레이터 글래스고우의 로그변환된 부동산 가격에 적용하여 조건부 자기회귀모형과 비교하였다.

• The Journal of Asian Finance, Economics and Business
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• 제9권2호
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• pp.363-370
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• 2022

The study aims to probe the impact of foreign ownership on Vietnamese listed firms' capital structure. This study employs panel data of 288 non-financial firms listed on the Ho Chi Minh City stock exchange (HOSE) and Ha Noi stock exchange (HNX) in 2015-2019. In this research, we applied a Bayesian linear regression method to provide probabilistic explanations of the model uncertainty and effect of foreign ownership on the capital structure of non-financial listed enterprises in Vietnam. The findings of experimental analysis by Bayesian linear regression method through Markov chain Monte Carlo (MCMC) technique combined with Gibbs sampler suggest that foreign ownership has substantial adverse effects on the firms' capital structure. Our findings also indicate that a firm's size, age, and growth opportunities all have a strong positive and significant effect on its debt ratio. We found that the firms' profitability, tangible assets, and liquidity negatively and strongly affect firms' capital structure. Meanwhile, there is a low negative impact of dividends and inflation on the debt ratio. This research has ramifications for business managers since it improves a company's financial resources by developing a strong capital structure and considering foreign investment as a source of funding.

• Smart Structures and Systems
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• 제31권1호
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• pp.29-43
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• 2023

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

• KDI Journal of Economic Policy
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• 제36권2호
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• pp.103-136
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• 2014

무위험 이자율 평형은 환율과 금리 간의 관계를 체계적으로 설명한 조건식으로서 개방거시분석 등에 널리 활용되고 있다. 그럼에도 불구하고 동 조건 성립 여부에 대한 검증은 국가별 거시경제 여건 등의 상이성 등으로 인해 뚜렷한 합의를 이루지 못하고 있다. 특히 글로벌 금융위기 전후 주요국의 정책대응은 국제금융시장에 큰 변화를 가져왔으며, 한국을 포함한 신흥국 시장에도 간접적으로 영향을 미쳤다. 이러한 관점에서 본고는 글로벌 금융위기 전후 우리나라 무위험 이자율 평형조건의 동태성을 국면전환모형으로 모델링하고 베이지안 MCMC 방식에 기반하여 추정한 다음, 추정 결과를 바탕으로 정책적 시사점을 제시하였다. 추정 결과, 세 개의 구조변화를 가정한 모형이 우리나라 이자율 평형조건의 동태성을 가장 잘 성명하는 것으로 나타났으며, 최근에는 평형조건의 불균형 정도가 크게 완화되고 변동성도 안정적인 상태에 진입한 것으로 분석되었다. 본고는 이러한 추정 결과를 얻은 주된 이유가 글로벌 유동성 증가에 따른 외국인 채권 차익거래와 단기 원화 및 외화 자금시장의 연계성 강화에 주로 기인한 것으로 판단하였다. 추정방식 측면에서 본고는 모형 내에 구조 변화를 가정하지 않는 기존 연구를 한층 더 정교하게 발전시켰다는 점에서 기존 연구와 차별화될 뿐만 아니라 추정 결과를 해석하는 과정에서 단기 원화 및 외화 자금시장의 성격 변화에 주목하였다는 점에서 기존에 시도하지 않은 무척 새로운 접근방식이라고 할 수 있을 것이다. 본고의 정책적 시사점은 이자율 평형조건의 동태성 등을 고려하여 정책당국이 시장모니터링을 더욱 강화하고 새로운 정책수단을 발굴할 필요가 있다는 점 등이다.