• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.145-159
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• 2011

National long-term care insurance started in July, 2008. We try to make up for weak points and develop a long-term care insurance system. Especially, it is important to upgrade the rating model of the category of need for long-term care continually. We improve the rating model using the data after enforcement of the system to reflect the rapidly changing long-term care marketplace. A decision tree model was adpoted to upgrade the rating model that makes it easy to compare with the current system. This model is based on the first assumption that, a person with worse functional conditions needs more long-term care services than others. Second, the volume of long-term care services are de ned as a service time. This study was conducted to reflect the changing circumstances. Rating models have to be continually improved to reflect changing circumstances, like the infrastructure of the system or the characteristics of the insurance beneficiary.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.101-110
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• 2010

In this study, a prediction model for incident reaction time was developed so that we can cope with the increasing demand for information related to the accident reaction time. For this, the time for dealing with accidents and dependent variables were classified into incident grade, A, B, and C. Then, fifteen independent variables including traffic volume, number of accident-related vehicles and the accidents time zone were utilized. As a result, traffic volume, possibility of including heavy vehicles, and an accident time zone were found as important variables. The results showed that the model has some degree of explanatory power. In addition, when the CHAID Technique was applied, the Answer Tree was constructed based on the variables included in the prediction model for incident reaction time. Using the developed Answer Tree model, accidents firstly were classified into grades A, B, and C. In the secondary classification, they were grouped according to the traffic volume. This study is expected to make a contribution to provide expressway users with quicker and more effective traffic information through the prediction model for incident reaction time and the Answer Tree, when incidents happen on expressway

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.137-146
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• 2011

Long-term care insurance is a social insurance system that provides benefits to the elderly who have difficulty taking care of themselves for a period of at least 6 months. This system was started in July, 2008 and it is very important to set proper judgement ratings for the approval process. We try to develop and improve the judgement rating system using decision tree models. Our tree model is found to be more stable and efficient than the previous one.

• Journal of Ginseng Research
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• v.33 no.3
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• pp.240-243
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• 2009

To better understand how to increase a ginseng grower's revenue, a 4-factor revenue determination model (RDM) of raw ginseng production (R=A Y Q P) was proposed. The total revenue (R) is a multiplicative function of four factors: cultivation area(A), unit yield (Y), quality grade (Q) and unit price (P). The A appears to be a pure capital factor. Y and Q are technological factors and P is social and market factor. When P is constant, the technological term (YQ) is the revenue per unit area (R/A) production efficiency per capital. The RDM appears to be a linear model between R and A with the slope [YQ]. RDM was applied to three farmers' raw ginseng production for assessment of its dependency on capital and technological factors.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.292-312
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• 2017

The purposes of this research are to verify: first, if the technology appraisal model reflects the company's management performance and the rates of bankruptcy and overdue; second, if the existing classification system of technology levels is suitable; and third, which is the most important appraisal factor that defines the classification system of technology levels. As a result of the analysis, financial performance (stability) and non-financial performance (technology environment) proved to be significant variables in explaining technology ratings. According to the verification of the suitability of classification system, it appeared that there is a significant difference in all appraisal items of all groups. The result of neural networks model verification indicates that the most important variable was the R&D capacity, the second variables which determine the suitability of technology financing were indicators related to the company management. The second variables which determine a company's technological excellence were a company's technological base. To summarize, the technology appraisal model not only reflects both managerial performance and risks of a company, but also anticipates the future by converging the management competence and technological competitiveness into R&D capacity. This implies that if the 'forward-looking' technology appraisal model is integrated into the existing, credit rating model, the appraisal model may have positive impact on improving anticipation and stability.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.426-426
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• 2023

대설은 일반적으로 해양과 대륙의 온도차가 큰 지역, 바다·호수와 같이 상대적으로 따뜻한 곳이 인접해 있어 기단 변질이 잘 일어나는 지역, 산악에 의해 습윤한 공기가 강제 상승되는 지역에서 자주 발생한다. 우리나라는 찬 대륙고기압 공기가 해수 온도 차로 눈 구름대가 만들어지거나, 고기압 가장자리에서 한기를 동반한 상층 기압골이 우리나라 상공을 통과하면서 대설이 발생한다. 최근 우리나라에서 빈번하게 발생하는 대설피해는 직접피해와 간접피해로 나뉘며, 이에 따라 사회·경제적으로 막대한 피해를 야기한다. 우리나라 대설피해양상은 지역적 특성, 방재 대책, 대처능력 등에 따라 달라지는 것이 특징이며, 지역적으로 다르게 발생하는 대설피해를 효과적으로 대비할 수 있는 연구가 필요하다. 따라서 본 연구에서는 지역적 특성을 고려한 차등화된 대설 피해를 예측하는 연구를 진행하고자 하였다. 본 연구에서는 기상요소 및 사회·경제적 요소 등을 입력자료로 활용하고, DPSIR 분석을 통해 Red Zone, Orange Zone, Yellow Zone, Green Zone으로 위험 등급을 분류 및 등급 별 대설피해 예측기법을 개발하였다. 최종적으로 1994년부터 2020년까지의 과거 대설 피해액 자료와 다중회귀분석을 이용하여 기법을 개발하였고, 기법의 예측력 평가를 위해 RMSE와 RMSE를 표준화한 NRMSE의 두 가지 통계 지표를 사용하여 평가하였다. 모형별 예측력 평가 결과 Yellow 등급 모형이 가장 우수한 예측력을 보였다. 추후 본 연구결과를 통해 대설피해 범위를 예측하는 연구가 진행된다면 사전에 대설피해에 대한 대응방안 수립과 지역별제설 우선순위를 결정할 수 있는 지표가 개발될 것으로 기대된다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.229-237
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• 2010

This study analyzes the factors that affect the safety condition level of road-bridges, one of the important infrastructures. Utilizing Binary Logit model, this report empirically identifies the key factors that has influenced the recent assessed safety condition level of the first and the second major types of road-bridges, managed by public agencies, and the changes of the safety level for last six years. As a result of the analysis, the most important factor that influences the safety condition level is not the physical characteristics, but the management quality. As road-bridges are getting older and older, the management quality tends to bring about more differentials in assessing the safety condition level. The safety condition level, C or D, is likely to be improved the level, A or B, is likely to become degraded. To achieve the goal that keep the safety condition level, A and B, more than 90%, it should be considered to make the degrading rate from B to C lower. However, this study includes the limitation on data. It is essential to collect structure data that are spread out in many agencies to complement the limitation for further research.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.154-154
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• 2017

전 세계적인 기후변동과 기후변화의 영향으로 대규모 인명 및 재산피해를 유발하는 자연재난의 빈도와 강도가 증가하고 있다. 이렇게 변화하는 상황에서 효율적인 대책을 수립하기 위해서는 재해에 노출된 특성을 지역적 특성과 함께 고려하여 지역별로 재해에 위험한 정도를 평가하는 것이 선행되어지고, 재난 피해 발생전에 피해 지역 및 범위를 예측하는 것이 필요하다고 판단된다. 따라서 본 연구에서는 국내 자연재난 피해의 65% 이상을 차지하는 호우피해를 대상으로 PSR(Pressure-State-Response) 구조를 이용하여 호우피해위험지수(Heavy rain Damage Risk Index, HDRI)를 제안하여 호우 위험도를 평가하고자하였다. 또한 도출된 지역별 위험등급에 따른 호우피해 예측함수를 개발하여 재해발생 전에 개략적인 피해의 범위를 예측하고자 하였다. 먼저 지역별 호우 위험도 평가를 위해 압력지표, 현상지표, 대책지표를 구축하고, 주성분분석을 이용하여 평가지표를 결정하였다. 결정된 평가지표를 동일한 가중치를 부여하여 호우피해위험지수를 도출하였다. 분석결과, 경기도 31개 지자체 중에서 가장 안전한 1등급인 지자체는 15개의 지자체로 나타났으며, 2등급인 지자체는 7개, 3등급인 지자체는 9개로 분류되었다. 지자체별 호우 위험도 등급에 따라서 재해기간별 총강우량, 재해일수, 선행강우량(1~5일), 지속시간별 최대강우량(1~24시간) 등의 자료를 설명변수로 구축하였고, 다중회귀모형과 주성분분석을 활용하여 예측함수를 개발하였다. 등급별 호우피해 예측함수는 N-RMSE가 12~18%로 호우피해를 적절하게 예측하는 것으로 평가되었다. 본 연구를 통해 지자체별 호우피해위험도 등급을 파악 할 수 있으며, 평가된 호우피해위험도 등급별로 호우피해 예측함수 개발을 통해 사전에 호우피해 발생 및 규모를 파악할 수 있게 되었다. 따라서 본 연구의 결과는 각 지자체 및 관련 부처에서 효과적인 방재체계를 수립하는데 있어 기초자료로 활용될 수 있을 것으로 판단된다.

• The Korean Journal of Financial Management
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• v.14 no.3
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• pp.263-287
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• 1997

본 연구에서는 국내은행의 위험도가 반영된 보험요율을 Merton에 의해 처음으로 제시된 예금보험요율 결정모형을 이용하여 추정하였다. 실증분석 결과에 의하면 표본은행간의 예금보험요율의 추정치에는 횡단면적 차이가 있는 것으로 나타나 표본기간 중 여러 은행들이 공격적 경영을 취함으로써 은행파산의 위험도를 높이는 도덕적 위해의 문제를 발생시켰음을 보여주고 있다. 본 연구는 상관관계 분석을 통하여 추정된 보험요율이 Moody's사의 국내은행에 대한 장기신용등급과 재무건전도등급, 그리고 은행규모, 수익성, 자본적정성, 자산건전성을 나타내는 지표들과 어떠한 관계에 있는 지를 살펴보았다. 분석결과에 의하면 Moody's사의 국내은행에 대한 장기신용등급, 재무건전도등급과 보험요율 사이에는 통계적으로 유의한 관계가 있는 것으로 나타나 추정된 보험요율이 이들 지표와 마찬가지로 위험도를 적절히 반영하는 것으로 나타났다. 또한 보험요율은 은행규모, ROA, ROE들과는 음의 관계가 있는 것으로 나타났으나, BIS기준 자기자본비율, 부실여신비율과는 양의 관계가 있는 것으로 나타났다. 그러나 자기자본비율이나 부실여신비율이 은행의 신용도나 위험도를 적절하게 반영하지 못하는 것으로 나타남으로써 이들 비율에 대한 회계방식의 개선이 요구됨을 본 연구의 결과는 보여주고 있다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.133-144
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• 2017

This study investigates firm-specific financial variables that determine investment or speculative grades from the viewpoint of firms, which are one of the major stakeholders related to the credit rating. We employ an ordered probit model for our analysis with the sample data from 1999 to 2015 for listed firms in the Korean stock markets. For investment grades, operating margin, sales, market-to-book, dividend payment, capital expenditure ratio, and tangible asset ratio have a significantly positive impact on credit ratings. In the subsample for speculative grades, the coefficients of the dividend payment, retained earnings ratio, and capital expenditure ratio are significantly positive while short-term debt ratio and R&D expenditures have a significantly negative impact on credit ratings. For the analysis before and after 2009, when the Credit Information Use and Protection Act was strengthened after the global financial crisis, the coefficients of the capital expenditure ratio, cash ratio, and tangible asset ratio are significantly positive in the subsample for investment grades before 2009, but not significant after 2010. The coefficient of the long-term debt ratio is more significantly negative than that of the short-term debt ratio before 2009, for speculative grades, but short-term debt ratio has a more negative effect on ratings than long-term debt ratio after 2010. Surprisingly, the coefficient of the R&D expenditures is significantly negative in both investment and speculative grades since 2010. Our findings are inconsistent with the conjecture that the increase in R&D expenditures enhances the possibility of creating cash-flow by raising the investment growth opportunity, and thus affects positively the credit rating.