• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.365-368
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• 1998

• 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 Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• Proceedings of the KOR-KST Conference
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• 2003.02a
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• pp.75-93
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• 2003

Hub-and-spokes전략은 교통분야에서 널리 이용되는 네트워크전략이지만, 철도의 경우 대개 이 전략을 이용하기 어려운 네트워크구조를 가지고 있어 그 적용사례를 찾아보기 어렵다. 그러나 유럽에서는 철도망이 도로망처럼 조밀하게 형성되어 있다는 점과 환적 처리기술의 발달로 90년대 초부터 이 전략이 철도화물운송부문에도 도입되기 시작했다. 이러한 관점에서 본 논문은 철도화물운송망에서의 hub-and-spokes전략을 구현하는 서비스네트워크 디자인모형을 개발하고, 모델의 실제철도망에의 적용성을 평가한다. 개발되는 모형이 전략모형임에도 불구하고 모형에서는 일반화된 운영비용 외에 열차속도, 서비스빈도, 터미널에서의 화물처리속도 등에 따른 시간지체비용도 고려되었다. 시간지체비용의 고려에 따라 야기되는 비선형 목적함수는 빈도별 서비스결정변수의 설정을 통해 선형화되어 결과적으로 모형은 선형 binary정수 최적화문제로 표현되었다. 규모가 큰 네트워크의 경우 해도출의 어려움 때문에 본 논문은 전체문제의 분할(decomposition)에 기초한 휴리스틱방법((heuristic method)으로 해결한다. 해도출의 효율성을 높이기 위해 서비스빈도개선과 관련하여 세 알고리즘이 개발되었다. 개발된 알고리즘은 유럽의 실제네트워크를 기초로 도출한 4개의 테스트문제에 적용되어, 해의 정확도와 해 도출의 효율성이 비교·평가되었다.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.351-362
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• 2012

Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.5 no.3
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• pp.191-197
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• 1993

Mathematical models of nonlinear waves due to disturbances moving with the near critical velocity in a basin of variable depth are discussed. A two-dimensional model for waves of arbitrary amplitude is developed. In the case of small perturbation it is shown that nonlinear ray method can be applied to obtain the generalized forced Korteweg-de Vries equation.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.475-485
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• 2014

A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.431-437
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• 1998

본 논문의 목적은 계층적 베이즈 일반화 선형모형을 이용하여 경시적 자료를 분석하는 것이다. 구체적으로 계층적 베이즈 변량효과 모형을 소개하고 무정보적 사전분포 하에서 사후분포가 진(proper)인지에 대한 충분조건을 찾는다 또한, 깁스(Gibbs) 표본자를 사용하여 제안된 계층적 베이즈 절차의 수행에 관해 논의한다. 현실자료를 사용하여 제안된 계층적 베이즈 분석을 예시하고, 이에 대응하는 경험적 베이즈 분석과 비교한다.

• 대한예방의학회:학술대회논문집
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• 2002.10a
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• pp.441-441
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• 2002

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.589-601
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• 2019

Various applications of radar rainfall data have been actively employed in the field of hydro-meteorology. Since radar rainfall is estimated by using predefined reflectivity-rainfall intensity relationships, they may not have sufficient reproducibility of observations. In this study, a generalized linear model is introduced to better capture the Z-R relationship in the context of bias correction within a Bayesian regression framework. The bias-corrected radar rainfall with the generalized linear model is more accurate than the widely used mean field bias correction method. In addition, we analyzed variability of the bias correction parameters under various geomorphological conditions such as the height of the weather station and the separation distance from the radar. The identified relationship is finally used to derive a regionalized formula which can provide bias correction factors over the entire watershed. It can be concluded that the bias correction parameters and regionalized method obtained from this study could be useful in the field of radar hydrology.