• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.515-525
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• 2010

We investigated design-based properties of the ordinary least square estimator(OLSE) and the weighted least square estimator(WLSE) in a panel regression model. Given a complex data we derive the magnitude of the design-based bias of two estimators and show that the bias of WLSE is smaller than that of OLSE. We also conducted a simulation study using Korean welfare panel data in order to compare design-based properties of two estimators numerically. In the study we found the followings. First, the relative bias of OLSE is nearly two times larger than that of WLSE and the bias ratio of OLSE is greater than that of WLSE. Also the relative bias of OLSE remains steady but that of WLSE becomes smaller as the sample size increases. Next, both the variance and mean square error(MSE) of two estimators decrease when the sample size increases. Also there is a tendency that the proportion of squared bias in MSE of OLSE increases as the sample size increase, but that of WLSE decreases. Finally, the variance of OLSE is smaller than that of WLSE in almost all cases and the MSE of OLSE is smaller in many cases. However, the number of cases of larger MSE of OLSE increases when the sample size increases.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.879-886
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• 2009

The purpose of this article is to present the application of Least Squares Support Vector Machine in analyzing the existing structure of brand. We estimate the parameters of the Market Share Attraction Model using a non-parametric technique for function estimation called Least Squares Support Vector Machine, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. Estimation by Least Squares Support Vector Machine technique makes it a good candidate for solving the Market Share Attraction Model. To illustrate the performance of the proposed method, we use the car sales data in South Korea's car market.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.779-787
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• 2007

This paper presents a highly efficient moving least squares finite difference method (MLS FDM) for a heat transfer problem of bi-material with interfacial boundary. The MLS FDM directly discretizes governing differential equations based on a node set without a grid structure. In the method, difference equations are constructed by the Taylor polynomial expanded by moving least squares method. The wedge function is designed on the concept of hyperplane function and is embedded in the derivative approximation formula on the moving least squares sense. Thus interfacial singular behavior like normal derivative jump is naturally modeled and the merit of MLS FDM in fast derivative computation is assured. Numerical experiments for heat transfer problem of bi-material with different heat conductivities show that the developed method achieves high efficiency as well as good accuracy in interface problems.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.744-751
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• 2003

A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.308-313
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• 2009

본 논문은 확장된 이동최소제곱 유한차분법을 이용하여 1차원 Stefan 문제를 해석할 수 있는 수치기법이 제시한다. 이동하는 경계의 자유로운 묘사를 위해 요소망이나 그리드 없이 절점만을 사용하는 이동최소제곱 유한차분법을 사용하였으며, 계면경계의 특이성을 모형화하기 위해 Taylor 다항식에 쐐기함수를 도입했다. 지배방정식은 안정성이 높은 음해법(implicit method)을 이용하여 차분하였다. 미분의 특이성을 갖는 이동경계를 포함한 반무한 융해문제의 수치해석을 통해 확장된 이동최소제곱 유한차분법이 높은 정확성과 효율성을 갖는 것을 보였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.719-722
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• 2011

본 연구에서는 이동최소제곱 차분법을 2차원 동적고체문제를 해석하기 위하여 확장시켰으며 Newmark ${\beta}$ 방법을 통해 explicit와 implicit 시간적분법을 모두 적용하여 그 차이를 비교하였다. 이동최소제곱 차분법은 Taylor 다항식을 이용하여 미분계산을 근사화 함으로써 내부 및 경계에서도 강형식을 그대로 이용할 수 있다. 그래서 계산이 빠르고 수치적분이 필요하지 않아 무요소법의 장점을 잘 살릴 수 있고 해석차수를 손쉽게 조정할 수 있어 cubic 등의 고차 근사계산이 간편하다. 두 가지 수치예제를 통하여 동적해석에 대한 이동최소제곱 차분법의 적용성과 안정성을 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.745-748
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• 2004

얼굴 이미지의 대부분은 표본의 수보다 특징 변수의 수가 많기 때문에 이러한 점을 고려한 특징 추출 방법이 필요하다. 본 논문에서는 부분 최소제곱법을 이용하여 특징 벡터의 차원을 축소하는 방법을 제안한다. 전통적인 차원 축소 방법인 주성분 분석은 클래스의 정보를 고려하지 않고 최대 변이를 가지는 성분을 추출하기 때문에, 클래스의 구분에 필요한 특징을 필수적으로 추출하지 못한다. 이에 비해, 부분 최소제곱법은 클래스 변수에 대한 정보를 포함하여 성분을 추출한다. 그러므로, 분류를 하는데 있어서는 주성분 분석에 의해 추출된 성분보다는 부분 최소제곱법에 의해 추출된 성분이 보다 더 예측적이다. 맨체스터와 ORL 얼굴 데이터베이스를 이용하여 실험한 결과, 분류와 차원 축소 측면에서 주성분 분석 방법보다는 부분 최소제곱법을 이용한 방법이 그 성능이 우수함을 알 수 있었다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.349-353
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• 2010

The existing calculation methods of the cadastral traverse survey was established in 1910s and are mostly outdated. The quality of these methods are not adequate to satisfy today's surveyor's needs that use a simple calculation method to distribute the error values. Thus, the main objective of this research is to find a methodology for appropriate calculation methods of the cadastral traverse survey that uses the general least square adjustment method with weight. Consequently, least square adjustment method has a better result than the previous one.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.