• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.299-320
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• 2007

We introduce a high breakdown point estimator referred to as data partitioning robust regression estimator (DPR). Since the DPR is obtained by partitioning observations into a finite number of subsets, it has no computational problem unlike the previous robust regression estimators. Empirical and extensive simulation studies show that the DPR is superior to the previous robust estimators. This is much so in large samples.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.557-578
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• 2007

In Response Surface Methodology (RSM), rotatability is a natural and highly desirable property. For second order general correlated regression model, the concept of robust rotatability was introduced by Das (1997). In this paper a new measure of robust rotatability for second order response surface designs with correlated errors is developed and illustrated with an example. A comparison is made between the newly developed measure with the previously suggested measure by Das (1999).

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1169-1182
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• 2006

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.531-550
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• 2023

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.267-280
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• 2007

In this paper, we consider the robust estimation for diffusion processes when the sample is observed discretely. As a robust estimator, we consider the minimizing density power divergence estimator (MDPDE) proposed by Basu et al. (1998). It is shown that the MDPDE for diffusion process is weakly consistent. A simulation study demonstrates the robustness of the MDPDE.

• 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.

• Journal of the Korean Mathematical Society
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• v.56 no.3
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• pp.689-701
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• 2019

We study the recovery results of ${\ell}_p$-constrained compressive sensing (CS) with $p{\geq}1$ via robust width property and determine conditions on the number of measurements for standard Gaussian matrices under which the property holds with high probability. Our paper extends the existing results in Cahill and Mixon from ${\ell}_2$-constrained CS to ${\ell}_p$-constrained case with $p{\geq}1$ and complements the recovery analysis for robust CS with ${\ell}_p$ loss function.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.1-6
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• 2005

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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• v.33 no.2
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• pp.149-157
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• 2004

The stationarity is one of the most important properties of a time series. We propose robust sign tests for seasonal autoregressive processes to determine whether or not a time series is stationary. The proposed tests are robust to the outliers and the heteroscedastic errors, and they have an exact binomial null distribution regardless of the period of seasonality and types of median adjustments. A Monte-Carlo simulation shows that the sign test is locally more powerful than the tests based on ordinary least squares estimator (OLSE) for heavy-tailed and/or heteroscedastic error distributions.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.281-286
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• 2003

The stationarity is one of the most important properties of a time series. We propose robust sign tests for seasonal autoregressive process to determine whether or not a time series is stationary. The tests have an exact binomial null distribution and are robust to the outliers and the heteroscedastic errors. Monte-Carlo simulation shows that the sign test is locally more powerful than the OLSE-based tests for heavy-tailed and/or heteroscedastic error distributions.