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Analyzing outliers: robust methods to the rescue

Nature Methodsvolume 16pages275276 (2019) | Download Citation


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

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  1. 1.

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

  2. 2.

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

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

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Author information


  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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Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Martin Krzywinski.

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