Title: S-estimation for penalized regression splines
Authors: Tharmaratnam, Kukatharmini ×
Claeskens, Gerda
Croux, Christophe
Salibian-Barrera, Matias #
Issue Date: 2010
Publisher: The Association
Series Title: Journal of Computational and Graphical Statistics vol:19 issue:3 pages:609-625
Abstract: This article is about S-estimation for penalized regression splines. Penalized regression splines are one of the currently most used methods for smoothing noisy data. The estimation method used for fitting such a penalized regression spline model is mostly based on least squares methods, which are known to be sensitive to outlying observations. In real-world applications, outliers are quite commonly observed. There are several robust estimation methods taking outlying observations into account. We define and study S-estimators for penalized regression spline models. Hereby we replace the least squares estimation method for penalized regression splines by a suitable S-estimation method. By keeping the modeling by means of splines and by keeping the penalty term, though using S-estimators instead of least squares estimators, we arrive at an estimation method that is both robust and flexible enough to capture nonlinear trends in the data. Simulated data and a real data example are used to illustrate the effectiveness of the procedure. Software code (for use with R) is available online.
ISSN: 1061-8600
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

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