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Pré-Publication, Document De Travail Année : 2018

Spline Regression with Automatic Knot Selection

Vivien Goepp
Olivier Bouaziz
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Résumé

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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Dates et versions

hal-01853459 , version 1 (03-08-2018)

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Vivien Goepp, Olivier Bouaziz, Grégory Nuel. Spline Regression with Automatic Knot Selection. 2018. ⟨hal-01853459⟩
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