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Analyzing outliers: robust methods to the rescue

Nature Methods volume 16, pages 275–276 (2019)Cite this article

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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  2. Farcomeni, A. & Greco, L. Robust Methods for Data Reduction (CRC Press, 2015).

  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

Authors
  1. Luca Greco
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  2. George Luta
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  4. Naomi Altman
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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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