• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.209-217
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• 2021

Although forest fires are more often triggered by artificial causes than by natural causes, the combustion conditions that spread forest fire damage over a large area are affected by natural phenomena. Therefore, using partial least squares structural equation modeling (PLS-SEM), which can analyze the dependent and causal relationships between various factors, this study evaluated the causal relationships and relative influences between forest fire, weather, and drought, taking Gangwon Province as our sample region. The results indicated that the impact of drought on forest fires was 27 % and that of the weather was 38 %. In addition, forest fires in spring accounted for about 60 % of total forest fires. This indicatesthat along with meteorological factors, the autumn and winter droughts in the previous year affected forest fires. In assessing the risk of forest fires, if severe meteorological droughts occur in autumn and winter, the probability of forest fires may increase in the spring of the following year.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.79-90
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• 2004

In this study, we applied the Bayesian Networks for the case of the trip generation models using the Seoul metropolitan area's house trip survey Data. The household income was used for the independent variable for the explanation of household size and the number of cars in a household, and the relationships between the trip generation and the households' social characteristics were identified by the Bayesian Networks. Furthermore, trip generation's characteristics such as the household income, household size and the number of cars in a household were also used for explanatory variables and the trip generation model was developed. It was found that the Bayesian Networks were useful tool to overcome the problems which were in the traditional trip generation models. In particular the various transport policies could be evaluated in the very short time by the established relationships. It is expected that the Bayesian Networks will be utilized as the important tools for the analysis of trip patterns.

• Korea Trade Review
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• v.42 no.2
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• pp.277-296
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• 2017

More than 95% of imports and exports in the World are being transported by vessels. In other words, marine transportation accounts for a large portion of share in the world trade. The purpose of this study is to analyze factors of seaborne trade volume according to items affecting on the world economy. This study conducted a linear regression analysis between seaborne trade volume and the world economy (world GDP) to estimate the correlation between them. Panel data analysis and random effects model analysis have been applied to examine the effect of seaborne trade volume. For this study, the seaborne trade volume is categorized into 10 items, and estimated how much global GDP will be affected when the trade volume changes. In addition, the granger causality test was conducted to verify the relationship between seaborne trade volume and the world GDP. As a result, seaborne trade volume and the world GDP were mutually influenced each other. However, seaborne trade volume affects the world economy more significantly. The items affecting world economic growth include petroleum products, crude oil, chemical products, and so on. The estimated value of the coefficients of petroleum products, crude oil and chemical products were 1.014, 1.013 and 1.010, respectively. The estimated value 1.014 of petroleum products means that the growth rate is 1.014 times higher than the current world GDP growth rate when the seaborne trade volume of petroleum products increased by one unit Lastly, this study examines the seaborne trade volume of 10 categories and then verifies whether the growth rate of world GDP will increase when the volume of seaborne trade increased. This study is expected to provide policy-makers with useful information about formulating policies related to international trade.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.697-712
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• 2010

This paper is concerned with the statistical analysis and development of stochastic models for the demand for life insurance policy loans. For these, firstly the characteristics of the regression trend, periodicity and dependence of the monthly demand for life insurance policy loans are investigated by a statistical analysis of the monthly demand data for the years 1999 through 2008. Secondly, the causal relationships between the demand for life insurance policy loans and the economic variables including unemployment rate and inflation rate for the period are investigated. The results show that inflation rate is main factor influencing policy loan demands. The overall evidence, however, failed to establish unidirectional causality relationships between the demand series and the other variables under study. Finally, based on these, univariate time series model and transfer function model where the demand series is related to one input series are derived, respectively, for the prediction of the demand for life insurance policy loans. A statistical procedure for using the model to predict the demand for life insurance policy loans is also proposed.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 1997.12a
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• pp.91-99
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• 1997

과거에는 환경오염에 의한 피해의 유무, 즉 인과관계를 규명하는 것이 일차적인 관심 대상이었다. 그러나 구체적이고 정량적인 정보를 요구하는 현대사회에서는 오염피해의 정도와 심각성을 평가하여 주민에게 알려 주어야 하며, 어느 정도의 오염수준을 우리사회에서 받아들일 수 있는가의 판단이 매우 중요한 문제로 떠오르게 되었다. 또한 복잡다기화 되어가고 있는 사회 현상속에서 이해관계와 불확실성으로 얽혀 있는 환경문제를 풀어나가기 위해서는 과학적이고 합리적인 방법론이 요구되고 있다. 이를 위해 제시될 수 있는 방법론이 위해성 평가(decision-making) 수단이나 연구의 한 분야로 지난 30여년 동안 비교적 빠르게 발전되어져 왔다. 우리나라의 경우도 이미 위해성 평가에 대한 연구가 계속 발전중에 있으며, 특히 수계에서 검출 가능하고 잠재적인 위해성을 지니는 수질오염물질에 대한 전반적인 위해성을 평가하여 우리나라 수질관리정책에 유용한 기초자료들을 지시한 바 있다. 본문에서는 수질중 chloroform을 대상으로 확률분포를 이용한 위해성 평가 방법론과 대기중 benzene을 대상으로 노출 허용량(margine of exposure) 접근법을 소개하고자 한다.

• Nuclear industry
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• v.23 no.6 s.244
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• pp.48-56
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• 2003

본 연구의 목표는 원전의 안전성 확보에 기여할 수 있는 조직 및 인적 요인의 평가에 대한 시스템 다이나믹스(System Dynamics) 모델을 만드는데 있다. 기존의 연구는 크게 인간 공학 혹은 확률론적 안전성 평가와 같은 공학적 방법과 조직 사회학적 접근으로 구분할 수 있다. 양 방법은 조직 및 인적 요인이 무엇인지를 밝혀주고 인적 실수를 줄이기 위한 지침을 제공해 준다. 그러나 인자들간의 상호 독립성의 가정은 원전에서 일어나고 있는 요인들간의 상호 작용을 설명하는데 어려움을 지닌다. 이러한 제약 사항을 극복하기 위해서 조직 및 인적 요인 사이의 인과 관계를 보여줄 수 있는 시스템 다이나믹스 모델을 개발하였다. 개발된 모델을 통하여 리더십, 직원 수의 조정, 각 부서별 업무량의 조정 등을 조작하면서 모델의 사용자들은 조직 측면에서 원전의 안전성이 어떻게 변화하는가를 확인할 수 있다. 시뮬레이션을 통해서 사용자들은 관리적인 시사점을 얻을 수 있을 것이다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.159-160
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• 2018

The domestic construction industry is changing its preference for demand and supply along with urbanization and economic development. Accordingly, initial risk assessments is more important than before. Currently, the research related to risk analysis except for apartment studies is insufficient. Therefore, the objective is to suggest a basic study on financial analysis model development by applying optimization method in residential officetel. To achieve the objective. first, the previous studies are investigated. Second, the causal loop diagram is structured based on the collected data. Third, the financial model is developed by using optimization method. In the future, the proposed model can be helpful whether or not conduct execution of an officetel development project to the decision makers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.1-9
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• 2012

Decisions on reliability screening rules and burn-in policies are determined based on the estimated reliability. The variability in a semiconductor manufacturing process does not only causes quality problems but it also makes reliability estimation more complicated. This study investigates the nonuniformity characteristics of integrated circuit reliability according to defect density distribution within a wafer and between wafers then develops optimal burn-in policy based on the estimated reliability. New reliability estimation model based on yield information is developed using a spatial stochastic process. Spatial defect density variation is reflected in the reliability estimation, and the defect densities of each die location are considered as input variables of the burn-in optimization. Reliability screening and optimal burn-in policy subject to the burn-in cost minimization is examined, and numerical experiments are conducted.

• KDI Journal of Economic Policy
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• v.36 no.2
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• pp.65-101
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• 2014

This paper estimates and characterizes expected inflations using an affine term structure model based on the empirical stochastic process of the interest rates in Korea. The empirical results show that the expected inflation which marked above 4% before the global financial crisis has dampened and stabilized after the crisis. Moreover, we investigate the rationality of the various expected inflation measures in terms of the unbiasedness and efficiency and find that unbiasedness is not rejected across the all measures, while the efficiency cannot be empirically warranted. Besides, we run Granger causality tests and conclude that the expected inflations compiled from the Consensus, BOK-Expert have the cross-causality with the long-run actual inflation, while the expected inflation estimated from the term structure model has the cross-causality with the short-run actual inflation. These results connote that expected inflations collected from different sources and methods have their targets and horizons and the central bank needs to watch all of them with a balanced view instead of preferring one to the other.