• Journal of Korean Society for Quality Management
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• v.6 no.1
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• pp.14-17
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• 1978

In response surface experiments, a polynomial regression model is often used to fit the response surface to explore the functional relationship between a response variable and several independent variables, and to determine the optimum operating conditions, which would be desirable to control the quality of industrial products. The problem considered in this paper is that of selecting subsets of polynomial terms from a given polynomial model so as to achieve "improved" response surfaces in estimation of the response. Such improvement in fitting the response surfaces would be very helpful to determine the optimum operating conditions and to explore the functional relationship with better precision. A criterion is proposed for selection of polynomial terms and illustrated with an industrial example.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.283-283
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• 2016

기후변화의 지역적 영향으로 호우의 강도와 빈도가 증가하고 있는 상황에서 수재해 대응을 위하여 다양한 기술들이 필요하며 특히 홍수 취약성에 대한 분석과 평가가 선행되어야 한다. 본 연구에서는 기존의 PSR(Pressure-State-Response) 모형과 DPSIR(Driving force-Pressure-StateImpact-Response 모형을 다중선형회귀 기법을 사용하여 최적화하였다(Fig. 1). 대상기간은 2008년부터 2013년까지이며, mod 1에서는 연도별로 다중선형회귀기법을 사용하여 최적 가중치를 산정하였고, mod 2에서는 대상기간(2008 ~ 2013) 전체에 대해 다중선형회귀기법을 사용하여 최적 가중치를 산정하는 방법을 적용하였다.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.121-131
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• 1998

이 논문의 목적은 비모수 회귀모형에 있어서의 오차의 분산을 추정하는 방법들 중 차분에 기저한 방법 (difference-based methods)을 이용한 기존의 추정량들을 비교 분석하는데 있다. 특히 점근적인 최적 이차 차분에 기저한 Hall과 Kay, Titterington(1990)의 HKT 추정량에 대한 그들의 추정량에 대한 문제점들을 제시하고, HKT추정량과, GSJS추정량, Rice추정량에 대하여 모의 실험을 이용하여 모수에 대한 수렴 속도를 비교 분석 하였다. 또한 GSJS 추정량에 대한 일치성과 수렴 속도를 보였다.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.643-656
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• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.219-219
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• 2017

상대적으로 유역의 관측 자료가 충분하지 못하거나 검증되지 않았을 경우 미계측 유역으로 정의되며 수문모형의 매개변수 검정을 할 수 없으므로 다른 방법을 고안해야 한다. 이를 위해 기존 연구에서는 지역적 특성을 고려한 지역회기분석을 통해 미계측 유역의 유량을 산정하였는데, 대부분 유역의 특성과 연 평균 유출량 자료의 관계를 이용한 회귀식으로 실시간 유량의 변화를 고려하기 어려웠다. 본 연구에서는 개념적 강우-유출모형으로 많이 사용되고 있는 개념적 수문모형인 GR4J의 매개변수에 대해 미계측 유역의 특성을 고려한 변수들을 이용하여 회귀식을 구하고 그 적용성을 평가하였다. 이를 통해 미계측 유역의 유량 시계열 자료를 생성할 수 있었다. 또한 IPCC에서 발간한 AR5의 RCP 4.5 시나리오를 적용하여 미래 유출량을 산정하였다. 우선 지역회귀분석을 적용하기 위해 수문모형을 이용한 계측 유역의 유출량을 구하였으며 22개의 전국 댐 상류 지점을 기준으로 SCE 알고리즘을 이용하여 GR4J의 최적 매개변수를 구하고 각 유역별로 물리적, 지형적, 기상학적 특성을 고려하여 11개의 변수를 선택하였다. 각 변수간 다중공선성(Multicollinearity)를 고려하기 위해 VIF(Variation Inflation Factor) test를 적용하여 최종 7개의 변수를 선정하고 단계별 회귀방법(Stepwise regression)을 이용하여 GR4J의 매개변수별 회귀식을 생성하였다.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.761-770
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• 2011

Robust principal components regression is suggested to deal with both the multicollinearity and outlier problem. A main aspect of the robust principal components regression is the selection of an optimal set of principal components. Instead of the eigenvalue of the sample covariance matrix, a selection criterion is developed based on the condition index of the minimum volume ellipsoid estimator which is highly robust against leverage points. In addition, the least trimmed squares estimation is employed to cope with regression outliers. Monte Carlo simulation results indicate that the proposed criterion is superior to existing ones.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.139-151
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• 2002

This study is concerned with model selection and diagnostics for nonlinear regression model through Bayes factor. In this paper, we use informative prior and simulate observations from the posterior distribution via Markov chain Monte Carlo. We propose the Laplace approximation method and apply the Laplace-Metropolis estimator to solve the computational difficulty of Bayes factor.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1481-1489
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• 2014

For predicting yearly total attendances in Korean probaseball games, ARIMA models have been widely adopted so far. In this paper, we discuss two other ways of ARIMAX and growth curves with an exogenous variable to predict the attendances. By using the exogenous variable, it turns out that the prediction has been improved compared to ARIMA. It is concluded that various statistical methods must be considered for better prediction, and its results can be applied to predict the attendances of other pro sports.

• Composites Research
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• v.17 no.2
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• pp.1-8
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• 2004

The present paper is concerned with the optimum design of taper spring, in which the static spring rate of the fiber-reinforcement composite material spring is fitted to that of the steel leaf spring. The thickness and width of springs were selected as design variables. The object functions of the regression model were obtained through the analysis with a common analytic program. After regression coefficients were calculated to get functions of the regression model, optimal solutions were calculated with DOT. E-glass/epoxy and carbon/epoxy were used as fiber reinforcement materials in the design, which were compared and analyzed with the steel leaf spring. The result of the static spring rates show that optimized composite leaf springs agree with steel leaf spring within 1%.