Outlier detection by means of robust regression estimators for use in engineering science


HEKİMOĞLU Ş., ERENOĞLU R. C. , KALINA J.

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A, vol.10, no.6, pp.909-921, 2009 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 6
  • Publication Date: 2009
  • Doi Number: 10.1631/jzus.a0820140
  • Journal Name: JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.909-921
  • Keywords: Linear regression, Outlier, Mean success rate (MSR), Leverage point, Least median of squares (LMS), Least trimmed squares (LTS), BREAKDOWN POINTS

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.