Communications for Statistical Applications and Methods

  • 제9권1호
  • /
  • Pages.291-304
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  • 2002
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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A Hybrid Algorithm for Identifying Multiple Outlers in Linear Regression

  • Kim, Bu-yong (Department of Statistics, Sookmyung Women′s University) ;
  • Kim, Hee-young (Strategy and Innovation Dept., Kookmin Credit Card Co. Ltd)
  • 발행 : 2002.04.01
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초록

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

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참고문헌

  1. The American Statistician v.45 Equivariant, Monotonic, 50% Breakdown Estimators Basset, Jr. G. W. https://doi.org/10.2307/2684377
  2. Journal of the American Statistical Association v.88 Procedures for the Identification of Multiple Outliers in Linear Models Hadi, A. S.;Simonoff, J. S. https://doi.org/10.2307/2291266
  3. Commun. Statist.-Theory Meth. v.19 A Monte Carlo Comparison of Five Procedures for Identifying Outliers in Linear Regression Kianifard, F.;Swallow, W. H. https://doi.org/10.1080/03610929008830300
  4. The Korean Communications in Statistics v.3 $L_{＼infty}$-estimation based Algorithm for the Least Median of Squares Estimator Kim, B. Y.
  5. Journal of Natural Sciences v.8 Improvements in Computational Efficiency and Accuracy of an Algorithm for the Identification of Regression Outliers Kim, B. Y.;Kim, S. B.
  6. Technometrics v.27 A Multistage Procedure for Detecting Several Outliers in Linear Regression Marasinghe, M. G. https://doi.org/10.2307/1270206
  7. Journal of the American Statistical Association v.79 Least Median of Squares Regression Rousseeuw, P. J. https://doi.org/10.2307/2288718
  8. Robust Regression nad Outlier Detection Rousseeuw, P. J.;Leroy, A. M.
  9. Journal of the American Statistical Association v.85 Unmasking Multivariate Outliers and Leverage Points Rousseeuw, P. J.;Zomeren, B. C. https://doi.org/10.2307/2289995
  10. Technometrics v.15 Testing for a Single Outlier in Simple Linear Regression Tietjen, G. L.;Moore, R. H.;Beckman, R. J. https://doi.org/10.2307/1267383
  11. Computational Statistics and Data Analysis v.36 A Comparative Analysis of Multiple Outlier Detection Procedures in the Linear Regression Model Wisnowski, J. W.;Montgomery, D. C.;Simpson, J. R. https://doi.org/10.1016/S0167-9473(00)00042-6

피인용 문헌

  1. A Criterion for the Selection of Principal Components in the Robust Principal Component Regression vol.18, pp.6, 2011, https://doi.org/10.5351/CKSS.2011.18.6.761