Robust nonparametric regression: A review
Cizek,Pavel Sadikoglu,Serhan
Cizek,Pavel
Sadikoglu,Serhan
Abstract
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.
Description
Date
2020-05
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Publisher
Research Projects
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Keywords
nonparametric regression, outliers, robust estimation
Citation
Cizek, P & Sadikoglu, S 2020, 'Robust nonparametric regression: A review', Wiley Interdisciplinary Reviews-Computational Statistics, vol. 12, no. 3, 1492. https://doi.org/10.1002/wics.1492