• 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.

• 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.

• 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.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.333-343
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• 1997

선형회귀분석에서 변수의 선택문제는 최적의 모형을 찾는데 아주 중요한 부분을 차지한다. George와 McCulloch(1993)는 계층적 베이즈 모형과 깁스표본법을 이용하여 선형회귀모형에서 변수를 선택하는 문제를 고려하였다. 이 논문에서는 George와 McCulloch의 모형을 바탕으로 각각의 설명변수가 모형에 포함될 사전확률을 객관적인 기준에 의하여 결정하는 문제를 고려하여 보았다.

• 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.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.755-765
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• 1998

그래프 대수선형모형은 계층적 대수선형모형의 부분집합이며 연관 그래프로 모형을 나타낼 수 있다. 또한 그래프 대수선형모형은 연관 그래프에서 엣지를 추가하거나 제거하는 것으로 분석을 수행할 수 있다. 본 연구에서는 그래프 대수선형모형이 가진 이러한 특징을 이용한 분석 시스템을 구현하였으며, 본 논문을 통해 이를 소개하고자 한다. 구현된 시스템은 분석자와 상호작용하며 분석결과를 시각적으로 평가할 수 있는 동적 연관 그래프를 제공하며, 단순한 마우스 조작에 의해 명령어 없이 자료입력만으로도 분석을 수행할 수 있도록 설계되었다. 또한 시스템은 자바 애플릿과 어플리케이션으로 구현되었기 때문에 월드 와이드 웹에서 운영할 수 있다.

• 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.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.227-232
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• 2015

The fuzzy linear programming(FLP) is the useful approach to many real world problems under uncertainty. This paper deals with a FLP whose objective value is fuzzy. And the right hand sides of convergent equality constraints are fuzzy numbers. We assume that the membership function of the objective value is piecewise linear and those of the right hand side are trapezoidal. Each of these trapezoidal functions can be algebraically replaced with three linear functions. Then the FLP problem is transformed into the Zimmermann's symmetric model. The fuzzy solution based on the max-min rule can be obtained by solving the crisp linear programming problem derived from the symmetric model. A numerical example has illustrated our approach. The application of our approach to the inconsistent linear system can enable generate us to get define the useful and flexible inexact solutions within acceptable tolerance. Further research is required to generalize the membership function.

• 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.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.