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Analyzing outliers: robust methods to the rescue

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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.

References

  1. Altman, N. & Krzywinski, M. Nat. Methods 13, 281–282 (2016).

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  3. Maronna, R., Martin, R. D., Yohai, V. J. & Salibian-Barrera, M. Robust Statistics: Theory and Methods (with R) (Wiley, 2018).

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Authors and Affiliations

  1. Assistant Professor of Statistics, the University of Sannio, Benevento, Italy

    Luca Greco

  2. Associate Professor of Biostatistics, Georgetown University, Washington, DC, USA

    George Luta

  3. Staff scientist, Canada’s Michael Smith Genome Sciences Centre, Vancouver, BC, Canada

    Martin Krzywinski

  4. Professor of Statistics, The Pennsylvania State University, University Park, PA, USA

    Naomi Altman

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  1. Luca Greco
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  2. George Luta
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  3. Martin Krzywinski
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  4. Naomi Altman
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Corresponding author

Correspondence to Martin Krzywinski.

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The authors declare no competing interests.

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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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