• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.571-580
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• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.603-612
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• 2002

Area of software measurement in software engineering is active more than thirty years. There is a huge collection of researches but still no concrete software development effort and cost estimation model. If we want to measure the effort and cost of a software project, we need to estimate the size of the software. A number of software metrics are identified in the literature; the most frequently cited measures are LOC (line of code) and FPA (function point analysis). The FPA approach has features that overcome the major problems with using LOC as a measure of system size. This paper presents simple linear regression model that related software development effort to software size measured in FP. The model is derived from the plotting of the effort and FP relation. The experimental data are collected from 789 software development projects that were recently developed under the various development environments and development methods. Also, the model is compare with other regression analysis model. The presented model has the best estimation ability among the software effort estimation models.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.537-549
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• 2023

The distribution characteristics of tidal flat sediment components are used as an essential data for coastal environment analysis and environmental impact assessment. Therefore, a reliable classification map of surface sedimentary facies is essential. This study evaluated the applicability of regression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim, various factors such as the number of field survey data and remote sensing-based auxiliary data, the effect of regression models on regression kriging, and the comparison with other prediction methods (univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV) data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a result of the case study, it was most important to secure an appropriate amount of field survey data and to use topographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sediment facies classification map. In addition, regression kriging, which can consider detailed characteristics of the sediment distributions using ultra-high resolution UAV data, had the best prediction performance compared to other prediction methods. It is expected that this result can be used as a guideline to produce the tidal flat surface sedimentary facies classification map.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1143-1150
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• 2017

The objective of this study is to examine the trends in information systems research. The abstracts of 1,245 articles were extracted from three leading Korean journals published between 2002 and 2016: Asia Pacific Journal of Information Systems, Information Systems Review, and The Journal of Information Systems. Time series analysis and topic modeling methods were implemented. The topic modeling results showed that the research topics were mainly "systems implementation", "communication innovation", and "customer loyalty". The time series regression results indicated that "customer satisfaction", "communication innovation", "information security", and "personal privacy" were hot topics, and on the other hand, "system implementation" and "web site" were the least popular. This study also provided suggestions for future research.

• Journal of the Korea Institute of Building Construction
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• v.10 no.5
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• pp.31-36
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• 2010

Rising interest rates positively affect investment in construction, while falling interest rates affect it negatively. In other words, the interest rate is one of the most critical factors affecting the construction sector. The purpose of this research is to analyze the relationship between the annual interest rate and construction contracts, and to present a model for quantitatively forecasting the economic performance of the construction sector. Based on the statistical data of interest rate changes for 19 years (from 1991 to 2009), this research induces an equation through regression analysis that incorporates interest rate and construction contract amounts as independent and dependent variables, respectively. The result of the analysis shows that, in the building and private sector, the interest rates are closely related to, with a correlation coefficient as high as 0.85. It was also indicated that the contract amounts of private and building sectors may increase quite rapidly in 2012.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.217-242
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• 2006

This Study reviewed 86 research articles using inferential statistics published in 2001-2004 in 4 korean core journals in the field of library and information science. Sampling methods, response rates and nonresponse bias, reliability test, and inferential statistic techniques used in the articles were critically reviewed and analyzed. Nonprobability sampling was mostly used. Average response rate was 74.47%. Parametric statistics were mostly used. Some misunderstandings in using each inferential statistics, especially Reliability Test, Multiple Regression, Factor Analysis, MDS, etc. were reported in this study.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.197-215
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• 2021

This paper analyzes the machine learning predictions of the economic effects of Busan's strategic industries on the employment and income using the Ridge Regression and Lasso Regression models with regulation terms. According to the Ridge estimation and Lasso estimation models of employment, the intelligence information service industry such as the service platform, contents, and smart finance industries and the global tourism industry such as MICE and specialized tourism are predicted to influence on the employment in order. However, the Ridge and Lasso regression model show that the future transportation machine industry does not significantly increase the employment and income since it is the primitive investment industry. The Ridge estimation models of the income show that the intelligence information service industry and global tourism industry are also predicted to influence on the income in order. According to the Lasso estimation models of income, four strategic industries such as the life care, smart maritime, the intelligence machine, and clean tech industry do not influence the income. Furthermore, the future transportation machine industry may influence the income negatively since it is the primitive investment industry. Thus, we have to select the appropriate economic objectives and priorities of industrial policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8719-8727
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• 2015

The purpose of this study is to examine the production efficiency of Rose farm and to explain the factors of the inefficiency. To analysis the production efficiency, SFA(Stochastic Frontier Analysis) and DEA(Data Envelopment Analysis) methods are measured, and then, Tobit regression model is used to analysis the influential factors on the production efficiency. As a result, first, the production efficiency by SFA is 88.4%, and by DEA, results are 78.5% and 85.2% in the CRS and VRS model, respectively. In particular, the production efficiency of the measurement results of the two methods are complementary, it is described in the same order of efficiency of each management body. Second, the results of tobit model shows that 6 input-factors are significant, and seed/nursery and material costs, which have the largest regression coefficient value and positive effect on production efficiency, are the most influential factors. Therefore, the results of this study indicates Rose farm can enhance their management efficiency by increasing amount of the seed/nursery and material costs.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1827-1831
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• 2010

국내의 저수지 비상대처계획수립은 저수용량 100만$m^3$이상의 중 대규모의 댐 및 저수지를 대상으로 수립되고 있다. 반면, 전국 14,208개의 시 군 구 지자체관리 저수지 가운데 80% 이상을 차지하고 있는 30만$m^3$ 미만의 중 소규모 저수지에 대해서는 피해규모의 정량화 방안이 체계적으로 구축되어 있지 못한 실정이다. 이에 본 연구는 중 소규모 농업용 저수지의 붕괴로 인하여 하류부에서 발생하는 피해를 산정하고, 잠재적인 피해액을 예측할 수 있는 홍수피해액 산정방법을 연구하고 인명 및 재산피해를 추정하는 기법을 연구하였다. 홍수피해액 산정에 대한 연구는 1970년대에 치수경제성 분석의 필요성이 대두되면서 원단위법(1993, 건설부, 하천시설기준), 회귀분석법(2001, 건설교통부, 치수사업 경제성분석 개선방안 연구), 다차원법(2004, 건설교통부, 치수사업 경제성분석 연구 방법) 순으로 발전되어 왔다. 원단위법과 회귀분석법의 문제점을 보완하기 위하여 다차원법이 제시되었지만 사용하는 자료가 방대하고 실제 가용성이 제한적인 자료를 포함하고 있어서 적용성이 떨어지는 단점을 가지고 있다. 본 연구에서는 중 소규모의 농업용 저수지 붕괴로 인한 홍수범람시의 피해액산정을 위하여 기존의 원단위법과 다차원법의 장점을 취하여 침수피해추정곡선법(IDEM : Inundation Damage Estimation Method)을 적용하였다. 인명손실액, 이재민피해손실액, 건물피해액, 건물내용물 피해액, 농작물 피해액, 농경지피해액, 공공시설물 피해액으로 구분하고 현재 제공되는 통계자료와 GIS 기법을 이용하여 대상 저수지인 창리저수지의 붕괴에 따른 소규모 범람구역의 침수심별 홍수피해액을 산정 후 침수심-피해액 곡선을 작성하였다. 창리저수지의 경우 저수지 붕괴시 0.4m의 침수심부터 제내지의 침수피해가 발생함을 알 수 있었으며, 침수심-피해액 곡선을 이용하여 침수심별 피해규모를 예상할 수 있었다. 향후 다양한 저수지에 적용성을 검토하여 국내 중 소규모 농업용 저수지 붕괴에 따른 홍수피해액 산정에 이용될 수 있을 것으로 판단된다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.