Outlier detection by means of robust regression estimators for use in engineering science. (English) Zbl 1178.62015

Summary: 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.


62F10 Point estimation
62F35 Robustness and adaptive procedures (parametric inference)
62J20 Diagnostics, and linear inference and regression
62J05 Linear regression; mixed models
65C05 Monte Carlo methods
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