• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.257-262
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• 2003

We introduce a new robust regression estimator, self-tuning regression estimator. Various robust estimators have been developed with discovery for theories and applications since Huber introduced M-estimator at 1960's. We start by announcing various robust estimators and their properties, including their advantages and disadvantages, and furthermore, new estimator overcomes drawbacks of other robust regression estimators, such as ineffective computation on preserving robustness properties.

• 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.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.63-73
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• 1995

We consider some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean. The proposed estimators are compared with the sample mean and subjective Bayes estimators in terms of "posterior robustness" and "procedure robustness".re robustness".uot;.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.291-308
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• 2024

The current article discusses ratio type exponential estimators for estimating the mean of a finite population in sample surveys. The estimators uses robust regression's Huber M-estimation function, and their bias as well as mean squared error expressions are derived. It was campared with Kadilar, Candan, and Cingi (Hacet J Math Stat, 36, 181-188, 2007) estimators. The circumstances under which the suggested estimators perform better than competing estimators are discussed. Five different population datasets with a well recognized outlier have been widely used in numerical and simulation-based research. These thorough studies seek to provide strong proof to back up our claims by carefully assessing and validating the theoretical results reported in our study. The estimators that have been proposed are intended to significantly improve both the efficiency and accuracy of estimating the mean of a finite population. As a result, the results that are obtained from statistical analyses will be more reliable and precise.

• Journal of applied mathematics & informatics
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• 제18권1_2호
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• pp.591-601
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• 2005

The results of fuzzy linear regression are very sensitive to irregular data. When this points exist in a set of data, a fuzzy linear regression model can be incorrectly interpreted. The purpose of this paper is to detect irregular data and to propose robust fuzzy linear regression based on M-estimators with triangular fuzzy regression coefficients for crisp input-output data. Numerical example shows that irregular data can be detected by using the residuals based on M-estimators, and the proposed robust fuzzy linear regression is very resistant to this points.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.331-348
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• 1998

In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1309-1317
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• 2006

The paper considers some Bayes estimators of the finite population mean with auxiliary information under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. The proposed estimators are quite robust in general. Numerical methods of finding Bayes estimators under these heavy tailed priors are given, and are illustrated with an actual example.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1191-1204
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• 2017

Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber's M estimation, bisquare estimation, and Yohai's MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.

• 품질경영학회지
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• 제19권1호
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• pp.51-64
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• 1991

This paper deals with a robust subset selection procedure based on Hodges-Lehmann estimators of location parameters. An improved formula for the estimated standard error of Hodges-Lehmann estimators is considered. Also, the degrees of freedom of the studentized Hodges-Lehmann estimators are investigated and it is suggested to use 0.8n instead of n-1. The proposed procedure is compared with the other subset selection procedures and it is shown to have good effciency for heavy-tailed distributions.

• Journal of applied mathematics & informatics
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• 제27권3_4호
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• pp.903-908
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• 2009

The shrink parameter in ridge regression may be contaminated by outlying points. We propose robust cross validation scores in ridge regression instead of classical cross validation. We use robust location estimators such as median, least trimmed squares, absolute mean for robust cross validation scores. The robust scores have global robustness. Simulations are performed to show the effectiveness of the proposed estimators.