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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2009, Vol. 10 Issue (6): 909-921    DOI: 10.1631/jzus.A0820140
Electrical & Electronic Engineering     
Outlier detection by means of robust regression estimators for use in engineering science
Serif HEKIMOGLU, R. Cuneyt ERENOGLU, Jan KALINA
Department of Geodesy and Photogrammetry Engineering, Yildiz Technical University, Istanbul 34349, Turkey; Department of Probability and Mathematical Statistics, Charles University, Praha 18675, Czech Republic
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Abstract  This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.

Key words： Linear regression      Outlier      Mean success rate (MSR)      Leverage point      Least median of squares (LMS)      Least trimmed squares (LTS)     
Received: 29 February 2008     
CLC:  O21  
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Serif HEKIMOGLU
R. Cuneyt ERENOGLU
Jan KALINA
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Serif HEKIMOGLU, R. Cuneyt ERENOGLU, Jan KALINA. Outlier detection by means of robust regression estimators for use in engineering science. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(6): 909-921.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A0820140     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2009/V10/I6/909

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