• 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.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.99-109
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• 2017

This research adopted a Robust Decision Making framework to re-evaluate four alternative strategies proposed by the Comprehensive Flood Management Plan for the Yeongsan River Basin report (MLTM, 2005) considering uncertainties of future floods under condition of climate change. To reflect the uncertainties, multiple sets of future flood scenarios were used with three uncertainty factors: the change in rainfall intensity based on the RCP climate change scenarios and the changes in the temporal and the spatial flood distributions. With combinations of these factors, 216 plausible flood scenario sets were generated and the performances of the four alternatives under different future states were evaluated. From the results, the most robust alternative among the strategies was identified. Moreover, the key factors which made the tested alternatives poor were discovered through assessment of the uncertainty factors. This information can provide detailed insights to decision makers and can be utilized to overcome alternatives' potential vulnerabilities by modifying the strategy to be more robust.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.39-39
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• 2017

한반도 기후변화 경향은 이미 기상 생태 환경 수자원 등 광범위한 부분에서 감지되고 있다(기상청, 2011a, 2011b). 현재까지의 연구에 따르면 한반도 기후변화에 따른 영향으로 강우패턴은 첨두강우가 7월에서 점차 8월로 이동 변화하는 것으로 전망되고, 양적으로 연강수량은 점차 감소가 전망되면서도 극한 값은 발생빈도와 크기가 점증할 것으로 전망되고 있다. 그래서 그간 기존댐에 대한 재평가(1998, 2010, 2012)와 발생 가능한 최대강우량 설계기준으로 기존 댐의 여수로 배제 능력을 증대시키는 비상여수로 설치 등 기존 댐 시설위주의 효율적인 기후변화 대응 또는 적응 방안을 시행해 왔다. 그러나, 기후변화로 인한 기상 상황은 전에 발생한 적이 없었던 새로운 기상이변과 재난을 가져오고 있다. 이에 기상 변화에 하나의 시설로서 대응 하던 방식에서 한 번의 기상이변이 유역 전반에 걸쳐 재난을 발생하는 최근의 상황에 맞추어 수자원 시설을 계획하는 방식에 대한 변화의 필요성 있다고 생각하였다. 이에 장래 전망되는 기후변화를 감안하여 이수와 치수 시설의 가뭄과 홍수에 대한 대처 능력을 유역 차원에서 평가하는 방법을 찾아보고자 한다. Robust 하다는 것은 어떤 상황에서도 작동이 되는 것을 말하는 강건한 계획으로, 이와 같은 시설 계획을 위해서는 먼저 현재의 시설물에 대한 회복력을 판단하는 평가가 있어야 할 것이다. 따라서, 용수공급이든 홍수 재난이든 회복력(복원력)에 대한 평가를 하고, 대안에 대한 로버스트 의사 결정 방법(RDM: Robust Decision Making)을 적용하여 우수한 대안을 찾으면 강건한 시설계획 수립이라는 절차가 될 수 있다고 판단하였다. 본 연구는 회복력(복원력)을 갖는 로버스트 의사결정방법에 대한 과거 연구 조사를 기초로 하여 연구 수행 절차를 마련한 후에 장래 한반도 기후변화 시나리오를 시범 유역에 적용하여 수자원 시설의 복원 또는 회복력을 분석하고, robust 의사결정방법을 적용함으로써, 향후 로버스트 수자원 시설 계획이 어떻게 이루어져야 하는지와 함께 이수와 치수 시설의 종합적인 계획 등에 대한 개념적인 절차와 방법의 제시를 도모하였다.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.459-466
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• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.47-55
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• 2011

Ratio and regression imputations depend on the model of a survey variable and the relation between the survey variable and auxiliary variables. If the model is not true, the unbiasedness of the estimator using the ratio or regression imputation cannot be guaranteed. In this paper, we develop the doubly robust imputation, which satisfies the approximate unbiasedness of the estimator, whether the model assumption is valid or not. The proposed imputation increases the efficiency of estimation by using the population information of the auxiliary variables. The simulation study establishes the theoretical results of this paper.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.147-157
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• 2007

Edge detection has been widely used in computer vision and image processing. We describe a new edge detector based on the robust rank-order test which is a useful alternative to Wilcoxon test. Our method is based on detecting pixel intensity changes between two neighborhoods with a $r{\times}r$ window using an edge-height model to perform effectively on noisy images. Some experiments of our robust rank-order detector with several existing edge detectors are carried out on both synthetic images and real images with and without noise.

• 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 Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.361-369
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• 2015

A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.