• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.387-395
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• 2007

We propose a doubly robustified estimating function for the estimation of parameters in the context of ARCH models. We investigate asymptotic properties of estimators obtained as solutions of robust estimating equations. A simulation study shows that robust estimator from specified doubly robustified estimating equation provides better performance than conventional robust estimators especially under heavy-tailed distributions of innovation errors.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.255-261
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• 2005

Many designed experiments require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• Journal of the Korean Statistical Society
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• 제11권2호
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• pp.109-117
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• 1982

In this paper, a preliminary study is performed on the subset selection procedures which are based on the trimmed means and the Hodges-Lehmann estimator derived from the Wilcoxon test. The proposed procedures are compared to the Gupta's rule through a small smaple Monte Carlo study. The results show that the procedures based on the robust estimators are successful in terms of efficiency and robustness.

• 응용통계연구
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• 제24권1호
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• pp.11-23
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• 2011

Robust unit root tests are developed for dynamic panels consisting of TAR processes. The test statistics are all based on diverse combinations of individual t-type tests for significance of TAR coefficients. Limiting null distributions are established. A Monte-Carlo experiment compares the proposed tests. The tests are applied to a panel data set of Canadian unemployment rates which show asymmetric features as well as having outliers.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.205-210
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• 2004

A weighted self-tuning robust regression estimator (WSTE) has the high breakdown point for estimating regression parameters such as other well known high breakdown estimators. In this paper, we propose to obtain standard quantities like confidence intervals, and it is found to be superior to the other high breakdown regression estimators when a sample is contaminated

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.18-20
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• 2006

The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to remove additive Gaussian noise contaminating images. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber's minimax norm. Our estimator is also optimal in the respect of maximizing the efficacy under the above noise environment.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.595-610
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• 2021

Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.

• Journal of the Korean Statistical Society
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• 제28권3호
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• pp.337-346
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• 1999

Simple correspondence analysis is a technique for giving a joint display of points representing both the rows and columns of an n$\times$p two-way contigency table. In simple correspondence analysis, the singular value decomposition is the main algebraic tool. But, Choi and Huh(1996) pointed out the singular value decomposition is not robust. Instead, they developed a robust singular value decomposition and provided applications in principal component analysis and biplots. In this article, by using the analogous procedures of Choi and Huh(1996), we derive a robust version of simple correspondence analysis.

• 응용통계연구
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• 제21권4호
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• pp.561-569
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• 2008

본 논문은 재무 시계열 자료에서 흔히 나타나는 이상치를 처리하기 위하여 이중 로버스트 추정함수를 제시하였다. 이중 로버스트 추정 방정식의 해인 로버스트 추정치를 이용하여 ARCH모형과 GARCH 모형 하에서 이상치를 처리하였다. 또한 실제 주가자료를 응용하여 기존의 최소제곱추정치보다 로버스트 추정치나 이중 로버스트 추정치의 성능이 우수함을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.201-215
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• 2000

The classical method for regression analysis is the least squares method. However, if the data contain significant outliers, the least squares estimator can be broken down by outliers. To remedy this problem, the robust methods are important complement to the least squares method. Robust methods down weighs or completely ignore the outliers. This is not always best because the outliers can contain some very important information about the population. If they can be detected, the outliers can be further inspected and appropriate action can be taken based on the results. In this paper, I propose a sequential outlier test to identify outliers. It is based on the nonrobust estimate and the robust estimate of scatter of a robust regression residuals and is applied in forward procedure, removing the most extreme data at each step, until the test fails to detect outliers. Unlike other forward procedures, the present one is unaffected by swamping or masking effects because the statistics is based on the robust regression residuals. I show the asymptotic distribution of the test statistics and apply the test to several real data and simulated data for the test to be shown to perform fairly well.