• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.517-525
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• 2011

This paper shows the performance evaluation of a robust estimator based on the GARCH model. We first introduce the method of a robust estimate in the GARCH model and the method of an outlier detection in the GARCH model. The results of the real internet traffic data show the out-performance of the robust estimator over the outlier detection method in the GARCH model. In addition, the method of the robust estimate is less complex than the method of the outlier detection method in the GARCH model.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.561-569
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• 2008

In this paper, we propose the double robust estimators which are the solutions of the double robust estimating equations to analyze and treat the outliers in the stock market data in Korea including the IMF period. The feasibility study shows that the proposed estimators work quitely better than the least squares estimators and the conventional robust estimators.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.981-996
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• 2008

Statistical matching techniques whose aim is to achieve a complete data file from different sources. Since the statistical matching method proposed by Rubin (1986) assumes the multivariate normality for data, using this method to data which violates the assumption would involve some problems. This research proposed the statistical matching method using robust regression as an alternative to the linear regression. Furthermore, we carried out a simulation study to compare the performance of the robust regression model and the linear regression model for the statistical matching.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3304-3310
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• 2010

This study solves the decision making problems for TFT-LCD manufacturing supply chain with demand and price uncertainties by establishing robust production and distribution strategies. In order to control the decisions regarding production graded by quality, inventory level and distribution, this study develop scenario model based stochastic mixed integer linear programs (SMILPs) that consider demand and price uncertainties as well as realistic constraints such as capacities etc. The performance of the solution obtained from the SMILPs using robust algorithms will be evaluated through various scenarios.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.80-83
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• 1988

In this paper a robust control law is presented which stabilezes overall system via pole reassignment and loop-shaping. A robust adaptive controller is designed combining this robust control law and a robust estimator.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.79-105
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• 2021

In the circumstance of standing out the climate change issue, the purpose of this study is to compare the efficiency of offshore and coastal fisheries according to whether or not greenhouse gas (GHG) emissions are considered, and then to present policy alternatives based on the analysis results. For analysis, the traditional data envelopment analysis (DEA), the slacks-based measure (SBM) and the SBM-undesirable models were used, and robust analysis of variance (ANOVA) and Wilcoxon Signed-rank tests were performed. As a result, the study showed that the average efficiency of fisheries decreased as the traditional DEA extended to the SBM model considering the slack and the SBM-undesirable model including the GHG emissions. Specifically, the average efficiency of the traditional DEA model, SBM model, and SBM-undesirable model was analyzed as 0.7350, 0.5820 and 0.4976 respectively. In addition, the results of the robust ANOVA and Wilcoxon Signed-rank tests all showed that there are statistically significant differences in efficiency between offshore and coastal fisheries as well as among traditional DEA, SBM and SBM-undesirable models. As a policy alternative to the analysis, it was suggested that to improve the efficiency of coastal and offshore fisheries, it is necessary to actively implement the new fishing vessel project and develop smart and electric hybrid fishing vessels.