Analyzing outliers: robust methods to the rescue

Robust regression generates more reliable estimates by detecting and downweighting outliers.

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Fig. 1: OLS and robust fit of the model W = –45 + 2H/3 + ε, with ε ~ N(0, 1) and a sample size of n = 11.
Fig. 2: Effect of the value of a single outlier on the OLS and robust fits.
Fig. 3: The effect of combinations of two outliers on OLS and robust fits.
Fig. 4: Identification of outliers using standardized residuals and Cook’s distance.


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Correspondence to Martin Krzywinski.

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Greco, L., Luta, G., Krzywinski, M. et al. Analyzing outliers: robust methods to the rescue. Nat Methods 16, 275–276 (2019). https://doi.org/10.1038/s41592-019-0369-z

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