Spline Regression with Automatic Knot Selection
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.
Cite
@article{arxiv.1808.01770,
title = {Spline Regression with Automatic Knot Selection},
author = {Vivien Goepp and Olivier Bouaziz and Grégory Nuel},
journal= {arXiv preprint arXiv:1808.01770},
year = {2025}
}