• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.367-382
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• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• 월간 기계설비
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• no.9 s.194
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• pp.45-53
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• 2006

정부는 8월 15일 광복절을 맞아 특별사면을 단행하고 정부, 지자체, 공기업 등 각 기관별로 지난 8월 15일부터 시행에 들어갔다. 이에 따라 영업정지,입찰참가 제한 등을 받고 있는 4,441개 건설사에 대한 제재조치가 해제된다. 또 자격정지 등 행정처분이나 벌점을 받고 있는 건설기술자 4,390명에 대한 처벌도 삭제된다. 반면 민,형사상 책임과 과징금,과태료,이행감제금 등의 납부 및 이행의무는 사면대상에서 제외된다.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.25-37
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• 2011

This research aimed to investigate whether commercial drivers' situation awareness ability is related to their frequency and magnitude of caused accidents and penalty points. For the purpose of measuring drivers' situation perception and interpretation capacities, two tests named 'situation awareness test' and 'hazard perception test' were developed. The tests were based on the data from 299 commercial drivers (test drivers) using a driving simulator. The outcome of drivers' performance on situation awareness and hazard perception tests was designed to be categorized into 5 grades, classifying the best as grade 1 and the worst as grade 5. As the result, low grades on situation awareness test had positive relationship with accumulated penalty points, frequency of accidents and safety index. Grades on hazard perception test were also positively correlated with accident frequency and safety indices. This suggests situation awareness ability of commercial drivers is significantly related to traffic violations and accident causing tendencies.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.131-142
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• 2017

The number of hospital admissions for pneumonia tends to increase annually and even more, pneumonia, the fifth leading causes of death among elder adults, is one of top diseases in terms of hospitalization rate. Although mainly bacteria and viruses cause pneumonia, the weather is also related to the occurrence of pneumonia. The candidate weather variables are humidity, amount of sunshine, diurnal temperature range, daily mean temperatures and density of particles. Due to the delayed occurrence of pneumonia, lagged weather variables are also considered. Additionally, year effects, holiday effects and seasonal effects are considered. We select the related variables that influence the occurrence of pneumonia using penalized generalized linear models.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.227-243
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• 2008

This study investigates an event detection method with the aim of generating an event-focused news summary from a set of news articles on a certain event using a multi-document summarization technique. The event detection method first classifies news articles into the event related topic categories by employing a SVM classifier and then creates event clusters containing news articles on an event by a modified single pass clustering algorithm. The clustering algorithm applies a time penalty function as well as cluster partitioning to enhance the clustering performance. It was found that the event detection method proposed in this study showed a satisfactory performance in terms of both the F-measure and the detection cost.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.16-28
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• 2017

In this study, we analyzed the result of new inspection, accidents, penalties, and operation records of the elderly driver's driving aptitude test in the city bus using the Transport Workers Management System and the Digital Tachograph Analysis System(eTAS). As a result of the analysis, the results of the driver aptitude test were similar when compared with the traffic company which has many bus drivers and the well - And it was confirmed that the average number of serious injuries and slight injuries in case of accidents was small. Comparing the number of dangerous driving behaviors per 100 km of driving, the elderly driver of the excellent company showed that the number of dangerous driving behaviors is considerably smaller than other elderly and non - elderly drivers, and that the result of correcting the wrong driving habits.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.863-874
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• 2021

In this paper, we propose a post-selection inference procedure for the fused lasso signal approximator (FLSA). The FLSA finds underlying sparse piecewise constant mean structure by applying total variation (TV) semi-norm as a penalty term. However, it is widely known that this convex relaxation can cause asymptotic inconsistency in change points detection. As a result, there can remain false change points even though we try to find the best subset of change points via a tuning procedure. To remove these false change points, we propose a post-selection inference for the FLSA. The proposed procedure applies a permutation test based on CUSUM statistic. Our post-selection inference procedure is an extension of the permutation test of Antoch and Hušková (2001) which deals with single change point problems, to multiple change points detection problems in combination with the FLSA. Numerical study results show that the proposed procedure is better than naïve z-tests and tests based on the limiting distribution of CUSUM statistics.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.12-20
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• 2019

This study analyzes and suggests improvement direction for the problems of housing related market laws and policies that cause sudden changes according to the application criteria. These laws and policies include income deduction from housing subscription, monthly rent loan for housing stability, high price house, penalty points from poor quality construction, and real estate brokerage commission. Each has one or more specific values that decide application criteria causing noticeable different results. Benefit or loss can be decided when an application range is changed by a small difference of the application value near to the boundary. This study suggest to use equations instead of a certain values to remove those sudden changes and to make them steadily increasing slopes which can be lines or curves. The concept can be applied to other laws and policies that have significantly different results between criteria.