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

查看全文 作　　者：Serif [1]HEKIMOGLU;R. Cuneyt [1]ERENOGLU;Jan [2]KALINA

机构地区：[1]Department of Geodesy and Photogrammetry Engineering,Yildiz Technical University;[2]Department of Probability and Mathematical Statistics,Charles University

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》索引2009年第10卷第6期,共13页

基　　金：Project (No. 28-05-03-03) supported by the Yildiz Technical University Research Fund, Turkey 摘　　要：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. 关键词：回归估计异常检测工程科学稳健学习管理系统模拟数据科学研究诊断工具

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

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