Robust linear regression: A review and comparison

By Chun Yu, Weixin Yao. 2014

254 citations recorded by the source corpus.

ABSTRACT 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.

Subfields: Statistics and Probability

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In Statistics and Probability

1. Edgar Dobriban
2. Yinjun Wu
3. Susan B. Davidson

Citations in the corpus

• DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “Methods based on this idea are some of the most statistically effcient ones for certain problems, see e.g., the review Yu & Yao (2017).”