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arXiv · 1808.01770

Spline Regression with Automatic Knot Selection

Vivien Goepp·Olivier Bouaziz·Grégory Nuel
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Abstract

In this paper we introduce a new method for automatically selecting knots in spline regression. The approach consists in setting a large number of initial knots and fitting the spline regression through a penalized likelihood procedure called adaptive ridge. The proposed method is similar to penalized spline regression methods (e.g. P-splines), with the noticeable difference that the output is a sparse spline regression with a small number of knots. We show that our method called A-spline, for adaptive splines yields sparse regression models with high interpretability, while having similar predictive performance similar to penalized spline regression methods. A-spline is applied both to simulated and real dataset. A fast and publicly available implementation in R is provided along with this paper.

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Vivien Goepp, Olivier Bouaziz, Grégory Nuel. 2018-08-06. Spline Regression with Automatic Knot Selection. https://doi.org/10.1016/j.csda.2024.108043

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