• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.569-578
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• 2020

Various graph clustering methods have been introduced to identify communities in social or biological networks. This paper studies the entropy-based and the Markov chain-based methods in clustering the undirected graph. We examine the performance of two clustering methods with conventional methods based on quality measures of clustering. For the real applications, we collect the mathematical subject classification (MSC) codes of research papers from published mathematical databases and construct the weighted code-to-document matrix for applying graph clustering methods. We pursue to group MSC codes into the same cluster if the corresponding MSC codes appear in many papers simultaneously. We compare the MSC clustering results based on the several assessment measures and conclude that the Markov chain-based method is suitable for clustering the MSC codes.

• 디지털산업정보학회논문지
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• 제5권4호
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• pp.109-116
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• 2009

In the paper, we designed an automatic security diagnostic evaluation System(SeDES) based on a security diagnostic evaluation model(SeDEM) for an organization's security assurance. The SeDEM evaluates a security level of an organization quantitatively by a security evaluation formula which is composed of security variables and security index as applying the statistical CAEL model for evaluate risk level of banks. The SeDES has a good expandability as changing security variables according to an organization scale, characteristics and so on. And it also has a excellent usage because it inputs only numeric data got from statistical technique to security index. We can understand more a security level correctly than the existent risk assessment system because it is possible to assess quantitatively with an security grade as well as score. analysis.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 추계 학술논문 발표대회
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• pp.122-123
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• 2018

Although the disaster rate of the industry as a whole is on a downward trend, the disaster rate of the construction industry is on an ongoing trend. Therefore, in this study, we analyzed safety accident statistical data of the construction site over the past three years. As a result of the analysis, the incidence of disasters at small construction sites was very high. And the proportion of disaster occurred for workers who worked in less than 6 months even roughly 92.6%. In addition, as a result of analyzing the form of disaster occurrence, the crash was 34.1% and the fall was 15.1%. The analysis results of these construction safety accidents are to provide as a basic material for developing a policy that can prevent safety accidents and a safety accident prediction model.

• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.389-407
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• 2006

Extreme value theory has been used widely in many areas of science and engineering to deal with the assessment of extreme events which are rare but have catastrophic consequences. The potential of extreme value theory has only been recognized recently in finance area. In this paper, we provide an overview of extreme value theory for estimating and assessing value at risk and expected shortfall which are the methods for modelling and measuring the extreme financial risks. We illustrate that the approach based on extreme value theory is very useful for estimating tail related risk measures through backtesting of an empirical data.

• Communications for Statistical Applications and Methods
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• 제27권2호
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• pp.201-210
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• 2020

Baseline-category logit random effects models have been used to analyze longitudinal nominal data. The models account for subject-specific variations using random effects. However, the random effects covariance matrix in the models needs to explain subject-specific variations as well as serial correlations for nominal outcomes. In order to satisfy them, the covariance matrix must be heterogeneous and high-dimensional. However, it is difficult to estimate the random effects covariance matrix due to its high dimensionality and positive-definiteness. In this paper, we exploit the modified Cholesky decomposition to estimate the high-dimensional heterogeneous random effects covariance matrix. Bayesian methodology is proposed to estimate parameters of interest. The proposed methods are illustrated with real data from the McKinney Homeless Research Project.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.655-671
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• 2021

This study analyzes the premium risk of insurers in Korea, which is expected to experience the fastest population aging in the world. Based on the Lee-Carter model, we generate 10,000 scenarios for the number of future survivors in the group of the 10,000 policyholders of life annuity. According to the result of simulation study, the probability of insurer's loss for both groups of male and female policyholders is very low. This result indicates that the premium risk of insurers is not as great as the insurer's concern. This study also suggests introduction of the longevity swap as an alternative to manage the premium risk for the insurer which sells life annuity products. The longevity swap allows insurers to hedge premium risk and reduce capital burden due to the premium risk inherent in life annuity. This study also shows through examples that the counterparty of swap deal may have excess profit in exchange for taking premium risk.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.161-169
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• 2021

The two-dimensional confusion matrix used in credit assessment, biostatistics, and many other fields consists of true positive, true negative, false positive, and false negative. Their rates, such as the true positive rate (TPR), true negative rate (TNR), false positive rate, and false negative rate, can be applied to measure its accuracy. In this study, we propose the TPR-TNR plot, a graphical method that can geometrically describe and explain these rates based on the confusion matrix. The proposed TPR-TNR plot consists of two right-angled triangles. We obtain that the TPR and TNR describe the acute angles of right-angled triangles in the plot. These acute angles can be used to determine optimal thresholds corresponding to lots of accuracy measures.

• 한국대기환경학회지
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• 제5권1호
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• pp.88-95
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• 1989

The aspects of the occurence frequency of $SO_2$ concentration were studied with the observed data in Seoul and the scheme that is capable of estimating not only highest concentration for a variety of averaging times but also concentrations for arbitary occurrence frequency with long term arithmatic mean and geometric standard deviation data, was evaluated. The results of the statistical analysis show that the occurrence frequency is almost log normal except a few cases, and 3rd highest values of daily mean concentration were about 4.2 $\sim$ 5.2 times higher than annual arithmatic mean. The evaluation with the observed hourly concentration shows that the scheme fairly well estimate the short concentration of arbitary occurrence frequence and it can be used for air quality management and environmental impact assessment.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.239-250
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• 2022

In many applications, we frequently encounter correlated multiple outcomes measured on the same subject. Joint modeling of such multiple outcomes can improve efficiency of inference compared to independent modeling. For instance, in developmental toxicity studies, fetal weight and number of malformed pups are measured on the pregnant dams exposed to different levels of a toxic substance, in which the association between such outcomes should be taken into account in the model. The number of malformations may possibly have many zeros, which should be analyzed via zero-inflated count models. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint modeling framework for continuous and count outcomes with excess zeros. In our model, zero-inflated Poisson (ZIP) regression model would be used to describe count data, and a subject-specific random effects would account for the correlation across the two outcomes. We implement a Bayesian approach using MCMC procedure with data augmentation method and adaptive rejection sampling. We apply our proposed model to dose-response analysis in a developmental toxicity study to estimate the benchmark dose in a risk assessment.

• 대한환경공학회지
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• 제32권1호
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• pp.47-52
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• 2010

국내 10개의 폐금속 광산을 대상으로 토양의 물리적, 화학적 및 생물학적 특성과 자생하고 있는 쑥(Artemisia princeps var. orientalis)의 카드뮴(Cd) 함량을 분석하였다. 식생이 없는 대조군 토양과 쑥 근권 토양의 Cd 함량은 산술평균으로 각각 5.92와 5.91 mg/kg으로 나타났으며, 분산분석 결과 두 토양간의 Cd 함량에는 유의한 차이가 없었다(p < 0.05). 그리고 쑥의 지상부와 지하부의 Cd 함량은 근권 토양의 Cd 함량과 유의한 상관관계를 보였다(p < 0.05, ${R^2}_{shoot}$ = 0.3120, ${R^2}_{root}$ = 0.4177). 쑥의 Cd 흡수 해석을 위하여 주성분 분석(PCA)을 실시한 결과, 최소 토양질 인자(MDS)로 유기물 함량 (OM), 탈수소효소 활성도(DHA), pH, 치환성 Mg가 선정되었다. MDS를 이용한 다중회귀분석 결과, 쑥의 지상부와 지하부의 Cd 함량의 회귀식에 대한 결정계수($R^2$)는 각각 0.3418과 0.5121로 나타났다. 이것은 MDS를 이용한 통계적인 토양질 평가가 식물의 중금속 흡수를 해석하는 유용한 기법이라는 것을 나타낸다.