• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1269-1278
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• 2014

Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and $D_s$-optimality.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.813-820
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• 2014

This study presents a numerical modeling of mean flow and turbulence structures of partly-vegetated open-channel flows. For this, Reynolds-averaged Navier-Stokes equations with vegetation drag terms are solved numerically using the non-linear k-${\varepsilon}$ model. The numerical model is applied to laboratory experiments of Nezu and Onitsuka (2001), and simulated results are compared with data from measurement and computations by Kang and Choi's (2006) Reynolds stress model. The simulation results indicate that the proposed numerical model simulates the mean flow well. Twin vortices are found to be generated at the interface between vegetated and non-vegetated zones, where turbulence intensity and Reynolds stress show their maximums. The model simulates the pattern of the Reynolds stress well but under-predicts the intensity of Reynolds stress slightly.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.475-484
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• 2018

Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.517-520
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• 2009

본 연구에서는 비선형 FEM 해석을 이용하여 PSC(Prestressed concrete)와 강으로 구성된 혼합구조 보의 휨 실험을 검증하였다. 혼합구조 보의 거동에 가장 큰 영향을 미치는 연결부는 Perfobond rib로 구성된 경우와 스터드로 구성된 경우를 고려하였다. 이종 재료가 접하는 경계면의 상호 작용에 대해 완전합성과 부분합성인 경우를 고려하여 혼합구조 보의 비선형 해석을 수행한 후 해석 결과와 실험 결과를 비교하였다. 하중-처짐관계, 파괴 형상 등의 해석 결과를 실험 결과와 비교한 결과, 부분합성을 고려한 모형이 실험 결과와 유사한 거동을 보였으며 또한 Perfobond rib를 갖는 실험체가 스터드를 갖는 실험체보다 해석 결과와 비교 시 안전측의 결과를 나타냈다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.371-381
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• 2000

In this paper, we describes smoothing spline in nonparametric regression and some asymptotic results for estimates of the regression cofficients in the parametric part were biased on semi parametric regression estimator.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.1
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• pp.170-177
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• 1989

The non-linear k-.epsilon. model developed by Speziale was employed for the prediction of developing turbulent flow in a square duct. The numerical procedure incorporated a finite volume method using a strong conservation form of the partially-parabolized Navier-Stokes equation. Results of the calculation were compared with available experimental data on the mean velocity field and turbulent kinetic energy, and was found to be in favorable agreement.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.167-167
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• 2018

토양수분은 토양에 포함된 평균 수분량을 나타내며 수문 순환 관점에서 매우 중요한 수문변량 중 하나이다. 본 연구에서는 대표적인 기계학습 방법인 Support Vector Machine (SVM)을 이용한 토양 함수 예측 기법을 개발하고자 하며, 예측인자로서 원격 탐측 기반의 토양함수자료, 강수량, 온도 등을 활용하고자 한다. SVM은 Kernel 함수를 이용하여 복잡한 비선형 관계를 선형 가정을 통해서 해석하는 기계학습 방법으로서 전역모델(global model)로서 다양한 수문기상분야에 적용이 이루어지고 있다. SVM의 장점은 일정 부분의 오차를 허용함으로서 모형의 일반화 측면에서 기존 인공신경망(artificial neural network, ANN)에 비해 우수한 성능을 나타내며, 특히 예측모형으로서 적용성이 매우 크다. 본 연구에서는 과거 토양 함수 자료와 강수, 온도, 위성 관측 기반 정보 등을 이용하여 모형을 적합시키고 이를 미계측 유역으로 확장하는데 연구의 목적이 있으며, 본 연구를 통해 제안된 모형은 용담댐 시험유역을 대상으로 적용되며 기존 ANN 모형 및 다중회귀분석 결과와 비교를 통해 모형의 적합성을 평가하고자한다.