• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 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
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• v.23 no.1
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• pp.207-218
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• 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 Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1019-1026
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• 1988

A parametric detection scheme for determenistic signals is obtained in a generalized observation model which contains non-additive noise. The model employed in this paper includes several special cases such as those describing purely-additive noise, multiplicative noise, and signal dependent noise and allows the consideration of deterministic and random signals. Locally optimum detectors for known deterministic signals in the model are derived and analyzed for performance. It is shown that the locally optimum detectors are interesting generalizations of those for the purely-additive noise model. Performance of the locally optimum detectors designed for the generalized observation model is compared to that of other common detectors.

• Fisheries and Aquatic Sciences
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• v.23 no.7
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• pp.19.1-19.14
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• 2020

Three generalized additive models were applied to the distribution of anchovy eggs and oceanographic factors to determine the occurrence of anchovy spawning grounds in Korean waters and to identify the indicators of their occurrence using survey data from the spring and summer of 1985, 1995, and 2002. Binomial and Gaussian types of generalized additive models (GAM) and quantile generalized additive models (QGAM) revealed that egg density was influenced mostly by ocean temperature and salinity in spring, and the vertical structure of temperature, salinity, dissolved oxygen, and zooplankton biomass during summer in the upper quantiles of egg density. The GAM and QGAM model deviance explained 18.5-63.2% of the egg distribution in summer in the East and West Sea. For the principle component analysis-based GAMs, the variance explained by the final regression model was 27.3-67.0%, higher than the regular models and QGAMs for egg density in the East and West Sea. By analyzing the distribution of anchovy eggs off the Korean coast, our results revealed the optimal temperature and salinity conditions, in addition to high production and high vertical mixing, as the key indicators of the major spawning grounds of anchovies.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.259-272
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• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

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

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

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