Robust linear regression: A review and comparison

Ordinary least-square (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may have a large effect on the parameter estimates. This article aims to review and describe some available and popular robust...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 46; no. 8; pp. 6261 - 6282
Main Authors Yu, Chun, Yao, Weixin
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 14.09.2017
Taylor & Francis Ltd
Subjects
Breakdown point
Computer simulation
Linear regression
Outliers
Outliers (statistics)
Robustness
Robustness (mathematics)
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Summary:Ordinary least-square (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may have a large effect on the parameter estimates. This article aims to review and describe some available and popular robust techniques, including some recent developed ones, and compare them in terms of breakdown point and efficiency. In addition, we also use a simulation study and a real data application to compare the performance of existing robust methods under different scenarios.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2016.1202271