Robust nonparametric regression: A review

Pavel Cizek*, Serhan Sadikoglu

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

26 Citations (Scopus)


Nonparametric regression methods provide an alternative approach to parametric estimation that requires only weak identification assumptions and thus minimizes the risk of model misspecification. In this article, we survey some nonparametric regression techniques, with an emphasis on kernel-based estimation, that are additionally robust to atypical and outlying observations. While the main focus lies on robust regression estimation, robust bandwidth selection and conditional scale estimation are discussed as well. Robust estimation in popular nonparametric models such as additive and varying-coefficient models is summarized too. The performance of the main methods is demonstrated on a real dataset. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods Statistical and Graphical Methods of Data Analysis > Nonparametric Methods.

Original languageEnglish
Article number1492
JournalWIREs Computational Statistics
Issue number3
Publication statusPublished - May 2020


  • nonparametric regression
  • outliers
  • robust estimation


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