• Title/Summary/Keyword: generalized additive model

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A Generalized Partly-Parametric Additive Risk Model

  • Park, Cheol-Yong
    • 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.

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A credit classification method based on generalized additive models using factor scores of mixtures of common factor analyzers (공통요인분석자혼합모형의 요인점수를 이용한 일반화가법모형 기반 신용평가)

  • Lim, Su-Yeol;Baek, Jang-Sun
    • 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.

A Study on Applying Shrinkage Method in Generalized Additive Model (일반화가법모형에서 축소방법의 적용연구)

  • Ki, Seung-Do;Kang, Kee-Hoon
    • 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.

On a Detection Scheme for Weak Deterministic Signals in Non-Additive Noise (비가산성 잡음에서의 약한 화정적 신호의 검파방식에 관하여)

  • Song, Iick-Ho
    • 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.

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Oceanographic indicators for the occurrence of anchovy eggs inferred from generalized additive models

  • Kim, Jin Yeong;Lee, Jae Bong;Suh, Young-Sang
    • 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.

Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data (트렌드와 계절성을 가진 시계열에 대한 순수 모형과 하이브리드 모형의 비교 연구)

  • Jeong, Chulwoo;Kim, Myung Suk
    • 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.

A Model-Fitting Approach of External Force on Electric Pole Using Generalized Additive Model (일반화 가법 모형을 이용한 전주 외력 모델링)

  • Park, Chul Young;Shin, Chang Sun;Park, Myung Hye;Lee, Seung Bae;Park, Jang Woo
    • 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.

Generalized Partially Linear Additive Models for Credit Scoring

  • Shim, Ju-Hyun;Lee, Young-K.
    • 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.

Factor Analysis of Customer Loyalty in Car Insurance Using Generalized Additive Partial Linear Model (일반화가법부분선형모형을 이용한 자동차보험 충성도 요인분석)

  • Ki, Seung-Do;Kang, Kee-Hoon
    • 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.

Tuning the Architecture of Support Vector Machine: The Case of Bankruptcy Prediction

  • Min, Jae-H.;Jeong, Chul-Woo;Kim, Myung-Suk
    • Management Science and Financial Engineering
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    • v.17 no.1
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    • pp.19-43
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    • 2011
  • Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.