• The Korean Journal of Applied Statistics
• /
• v.23 no.1
• /
• pp.207-218
• /
• 2010

Generalized additive model(GAM) is the statistical model that resolves most of the problems existing in the traditional linear regression model. However, overfitting phenomenon can be aroused without applying any method to reduce the number of independent variables. Therefore, variable selection methods in generalized additive model are needed. Recently, Lasso related methods are popular for variable selection in regression analysis. In this research, we consider Group Lasso and Elastic net models for variable selection in GAM and propose an algorithm for finding solutions. We compare the proposed methods via Monte Carlo simulation and applying auto insurance data in the fiscal year 2005. lt is shown that the proposed methods result in the better performance.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.2
• /
• pp.235-245
• /
• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• The Korean Journal of Applied Statistics
• /
• v.25 no.1
• /
• pp.67-79
• /
• 2012

The car insurance market in Korea has already entered (or is in the process of entry) a mature market that is characterized by increased competition by market participants. Participants are expected to compete more intensively in order to survive. Together with a slowdown in market growth the goal of non-life insurers' marketing strategies is to enhance existing customer loyalty because it is easier to raise their loyalty via customer satisfaction than to attract new customers in a stagnant market. In this article, we investigate what factors affect customer loyalty, and suggest some specific ways to establish and implement marketing strategies. We use a generalized additive partial linear model in order to find some significant factors.

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1215-1227
• /
• 2009

Underwriting refers to the process that the insurance company measures the potential risk of the future clients and decide whether insuring them with current premium. Although the traditional underwriting system used in Korean automobile insurance market is easy to understand, it is not based on a reliable statistical procedure. In this paper, we propose to apply the generalized additive model into construction of underwriting system, which is based on statistical analysis. We use automobile insurance data in Korea and apply our approach to the data. The results from the empirical analysis would be useful even for determining the significance of each variable in calculating automobile insurance premium.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1215-1224
• /
• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.11
• /
• pp.445-452
• /
• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.458-458
• /
• 2021

하천의 환경기능과 생태에 관한 관심이 증가하며 생태를 고려한 하천 유지 유량의 산정이 필요하다. 수문 특성의 물리적 서식처에 관한 연구는 PHABSIM, River2D 등의 소프트웨어 적용을 통한 유지유량증분법(IFIM), 수문 인자별 서식처적합도지수(HSI)를 기반으로 연구되고 있지만, 하천의 수질을 고려한 서식처 연구는 각 수질 인자별 서식처적합도지수 자료의 부족으로 하천유지유량 산정에 반영이 어려운 실정이다. 본 연구는 국내 하천의 수질·생태 모니터링 자료를 바탕으로 수온, DO 등의 수질 인자에 대한 피라미의 서식처적합도지수를 권역 단위로 산정했다. 수질 및 어류조사 자료는 물환경정보시스템의 최근 10년 수질측정망, 생물측정망 조사자료를 이용해 구축하였다. 피라미의 수질별 서식 적합도는 일반화가법모형(GAM)을 적용하여 수질 인자별 어류 개체 밀도 분포의 상관관계를 분석하여 지수화하였다. 특히, 어류의 서식 특성은 수계별로 상이할 수 있어 가용 데이터의 범위를 고려하여 권역별 수질 인자에 따른 피라미의 서식 특성을 분석하였다. 본 연구로 제시된 권역단위 피라미의 수질 서식처 적합도 지수는 생태를 고려한 하천사업의 계획, 평가의 기초자료를 제시할 수 있을 것이다. 또한, 피라미 외 각 하천의 주요 생태 어종 평가를 위한 수질 서식처 적합도지수 산정의 자동화를 위한 알고리즘 개발에 적용가능할 것으로 예상된다.

• Journal of the Korean Data and Information Science Society
• /
• v.7 no.2
• /
• pp.257-262
• /
• 1996

When we are estimate the smoothing parameter in spline regression model, we deal the diagnostic of influence observations as posteriori analysis. When we use Generalized Maximum Likelihood Function as the estimation method of smoothing parameter, we propose the diagnostic measure for influencial observations in the obtained estimate, and we introduce the finding method of the proper smoothing parameter estimate.

• The Korean Journal of Applied Statistics
• /
• v.22 no.2
• /
• pp.271-280
• /
• 2009

For bivariate binomial data with both intra and inter-class correlation, Danaher and Hardie (2005) proposed a bivariate beta-binomial model. However, the model is limited to the situation where the intra-class correlation is strictly positive. Thus it might be seriously inadequate for data with a negative intra-class correlation. Several authors have considered generalized binomial distributions covering a wider range of intra-class correlation which could relax the possible model restrictions imposed. Among others there are the additive/multiplicative and the beta/extended beta binomial model. In this study, bivariate models of the Sarmanov (1966) type are formed by combining each of those univariate models to take care of the inter-class correlation, and are evaluated in terms of the goodness-of-fit. As a result, B-mB and B-ebB are fitted, successfully, to real data and that B-mB, which has a wider permissible range than B-ebB for the intra-class correlation is relatively preferred.

• The Journal of Small Business Innovation
• /
• v.20 no.2
• /
• pp.35-50
• /
• 2017

The aims of this study is to analyze the effectiveness of firms financial status quo and the scale of financial support on SMEs overall performance. We have gathered the financial guarantee data from 1998 to 2013, provided by Korea Credit Guarantee Fund (KODIT), to analyze the effectiveness of Financial policy. To classify both financial status quo and scale of financial support, we utilized the following variables; Interest Coverage Ratio (ICR) and newly guaranteed amount ratio. To take the measurement of the overall performance, we employed profitability, growth ratio and activity index. To minimize the effect of repeated financial support (redundancy benefits), firms were selected based on the following criteria: firms that receive no financial support prior to implementing such policy over the last 3 years and no new financial support over the last 2 years. Results suggest that firms with higher ICR and large newly guaranteed amount influence on financial performance in terms of profitability index. Firms with lower ICR and large scale financial support showed a better performance compare to firms with small-scale financial support. Firms with large-scale financial support, irrespective of ICR inclined to have better performance to those of small-scale financial support in terms of growth index. For activity index, however, firms with large scale support led to higher performance in the short term. In turn, our analysis presents objective perspective with respect to the effectiveness of financial policy through credit guarantee on overall performance of SMEs. This study, therefore, implies that well-balanced SMEs supporting policy may lead to better directions.