• Journal of the Korean Geophysical Society
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• v.5 no.2
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• pp.131-142
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• 2002

Geomagnetic transfer function is generally estimated by choosing transfer to minimize the square sum of differences between observed values. If the error structure sccords to the Gaussian distribution, standard least square(LS) can be the estimation. However, for non-Gaussian error distribution, the LS estimation can be severely biased and distorted. In this paper, the Gaussian error assumption was tested by Q-Q(Quantile-Quantile) plot which provided information of real error structure. Therefore, robust estimation such as regression M-estimate that does not allow a few bad points to dominate the estimate was applied for error structure with non-Gaussian distribution. The results indicate that the performance of robust estimation is similar to the one of LS estimation for Gaussian error distribution, whereas the robust estimation yields more reliable and smooth transfer function estimates than standard LS for non-Gaussian error distribution.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.475-488
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• 2004

Several robust regression estimators are compared under contamination. Symmetric and asymmetric contamination schemes are used to measure the variance and MSE of regression estimators. Under asymmetric contamination depth-based regression estimator, especially projection based regression estimator(rcent) outperforms the rest and under symmetric contamination HBR performs relatively well.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.747-753
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• 1997

Function approximation from a set of input-output parirs has numerous applications in scientiffc and engineer-ing areas.Multiayer feedforward neural networks have been proposed as a good approximator of noninear function.The back propagation (BP) algorithm allows muktiayer feedforward neural networks oro learn input-output mappongs from training samples.However, the mapping acquired through the BP algorithm nay be cor-rupt when errorneous trauning data are employed.In this paper we propose a robust BP learning algorithm that is resistant to the errormeous data and is capable of rejecting gross errors during the approximation process.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.135-137
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• 2018

다수의 원유 선적항으로부터 여러 정유공장으로 수송된 원유를 정제하여 생산한 제품유를 글로벌 수요시장으로 배분하는 2단계 석유물류네트워크의 최적화는 글로벌 석유 메이저의 중요한 의사결정문제이다. 본 연구는 제품유 시장의 가격 및 수요 변동의 영향을 반영하여 원유 수송, 정제 및 제품유 배분을 최적화하기 위한 석유물류네트워크의 로버스트 추계적 모형을 제시하고 있다. 계산실험은 제품유 시장의 가격 및 수요 변동에 관한 시나리오 기반의 데이터를 사용하여 최적화 모형에 적용하였으며, 그 결과를 바탕으로 제시한 최적화모형의 유용성과 타당성을 검증하여 보고하고 있다.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.479-490
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• 1999

선형시계열모형인 AR(1)모형과 비선형시계열모형인 RCA(1), ARCH(1)모형에서 이상치(Outlier)가 존재할 경우 최소제곱추정량과 M추정량간의 점근상대효율(Asymptotic Relative Efficiency: ARE)을 구하여 두 추정량의 로버스트 성질을 비교·분석하였다. 또한 여러 유계함수(Huber, Tukey, Andrews, Hampel)들을 M추정함수에 적용하여 각각의 유계함수들을 비교·분석하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.91-95
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• 2003

본 논문에서는 정보 전달 능력이 향상된 변형된 축차확률비검정을 소개한다. 새로운 검정은기존의 검정과 수학적인 면에서 공동된 점들을 갖고 있으나, 이상치의 영향을 덜 받고 더욱이 그 존재에 대하여 시각적으로 보여주며, 또한 자료의 변화에 보다 민감하게 반응하는 특성이 있다. 새롭게 제안된 로버스트 축차확률비검정 시스템을 Microsoft사의 Visual Basic 6.0 언어로 구현하여 본 연구에서 제안한 모델과 기존의 검정모델을 비교한 결과 제안된 검정 모델의 우수성과 실용가능성을 실증적으로 확인하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.275-280
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• 2003

공간 자료에서 이상점이 존재할 경우 변이도(Variogram)를 추정함에 있어 그 효과를 줄이기 위한 방법으로 로버스트(robust) 변이도를 이용한다. 그러나 이상점이 존재하는 자료분석에서 로버스트 변이도를 사용하기에 앞서 이상점을 수정한 자료를 사용하였을 경우 그 효율성 또한 좋다고 알려져 있다. 본 논문에서는 이상점이 존재하는 자료를 분석함에 있어 기존의 이상점 수정법 및 새로운 이상점 수정법의 효율성을 비교하였다.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.471-485
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• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.917-926
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• 2013

This study presents the feasibility of fuzzy multi-criteria decision making (MCDM) techniques for the robust prioritization of projects. It is applied to water resources planning problem. Results from weighted sum method (WSM), analytic hierarchy process (AHP), revised analytic hierarchy process (R-AHP), and TOPSIS are compared with those from Fuzzy WSM, Fuzzy, AHP, Fuzzy R-AHP, and Fuzzy TOPSIS. For the calculation, all weights on criteria and the normalized data were obtained from the same investigation. As a result, the rankings from four MCDM techniques are slightly different while those from fuzzy MCDM show the comparatively consistent ranking. Therefore, it is desirable to use fuzzy MCDM technique when MCDM is used for the prioritization problem, since fuzzy MCDM can include the uncertain variability of input data and weighting values on criteria.