• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.303-309
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• 1993

In this paper we have defined two approaches to be error-robust when the precise form of error-structure is unknown. An experiment is optimal by the first criterion if it maximizes the minimum effciency over all candidates of error structure and is optimal by the second if it maximizes the minimum average of the efficiency over all candidates of error structure. In order to appreciate the basic implications of each design criterion, these approaches are applied to two different experimental situations, D- and heteroscedastic G-optimalities.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.561-571
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• 2007

Local linear regression estimator is the most widely used nonparametric regression estimator which has a number of advantages over the traditional kernel estimators. It is well known that local linear estimator can produce erratic result in sparse regions in the realization of the design and the interpolation method of Hall and Turlach (1997) is the very efficient way to resolve this problem. However, it has been never pointed out that Hall and Turlach's interpolation method is very sensitive to outliers. In this paper, we propose the robust version of the interpolation method for adapting to sparse design. The finite sample properties of the method is compared with Hall and Turlach's method by the simulation study.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.255-268
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• 1992

We propose an iterated interpolation approach for the estimation fo time series parameters in the presence of outliers. The proposed approach iterates the parameter estimation stage and the outlier detection stage until no further outliers are detected. For the detection of outliers, interpolation diagnostic is applied, where the atypical observations by the one-step-ahead predictor instead of downweighting is also proposed. The performance of the proposed estimation methods is compared with other robust estimation methods by simulation study. It is observed that the iterated interpolation approach performs reasonably well is general, especially for single AO case and large $\phi$ in absolute values.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1135-1148
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• 2018

Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.43-56
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• 2005

In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.2023-2032
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• 1993

This study suggests a new type shaft generating system driven by hydrostatic transmission suitable for small size vessels. Since the shaft generating system is affected ceaselessly by disturbances such as speed variation in pump driving speed and variation in external load, a robust servo control must be implemented to obtain stable electric power with constant frequency. Thus, in this study, a digital robust servo control algorithm is applied to the controller design. By the experiment and the numerical computation, the frequency variation characteristics of the generating system under various disturbances are investigated. Conclusively, it is said that the shaft generating system proposed in this study shows excellent control performances.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.8-9
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• 2011

본 논문에서는 수상 및 수중운동체의 안정성 및 안정화기법에 관해 고찰한다. 선박이 운동을 하게 되면 부가질량이 변하게 되고 대칭인 시스템행렬이 비대칭이 된다. 비대칭성에 따라 시스템의 안정성해석방법도 달라지는데 예를 들어 가속도 피드백을 통해 비대칭요소를 제거하여 대칭으로 변환시키는 것이 가장 대표적인 해석 및 안정화 기법이다. 시스템 모델자체는 어디까지나 모델이기 때문에 대상시스템을 명확하게 수식으로 표현할 수 없으므로 피드백에 의한 비대칭요소를 소거시키는 방법은 타당하지 못하다. 따라서 본 논문에서는 대칭행렬이 비대칭행렬로 변하는 제약에 구애받지 않는, 보다 일반성을 갖는 안정성해석법을 제안한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.151-157
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• 2003

OLS로 알려진 기존의 주절 방법은 변수수의 증가에 따라 다중공선성(Multicollinearity)의 문제와 더불어 해석력(Interpretability)이 떨어지는 문제를 가지게 된다. 본 연구에서는 파라미터의 절대값의 크기(L1-Norm)에 제약을 줌으로써 이와 같은 OLS의 문제를 해결할 수 있는 동시에, 잔차의 제곱합대신 절대오차를 사용하는 Least Absolute Value(LAV) 방법을 사용함으로써 이상치에 로버스트한 결과를 주는 방법론을 제안한다. 또한. 본 연구에서 제안하는 방법이 선형계획법에 의해 모델처럼 될 수 있는 특성으로 인해 제약조건이 있는 이차 형태의 최적화 문제보다 수행 속도면에서 뛰어난 결과를 주는 것을 수치예제을 통해 보인다.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.55-69
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• 2003

In this article, we propose the weighted LAD (least absolute deviations) estimators for multi-dimensional contingency tables and drive an estimation method to estimate the proposed estimators. To illustrate the robustness of the estimators, simulation results are presented for several models Including log-linear models and models for ordinal variables in multidimensional contingency tables. Examples were also introduced.