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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.
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Preprints, Working Papers, ...
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Contributor : Vivien Goepp <>
Submitted on : Friday, August 3, 2018 - 1:32:15 PM
Last modification on : Saturday, April 3, 2021 - 3:29:39 AM
Long-term archiving on: : Sunday, November 4, 2018 - 1:24:46 PM


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  • HAL Id : hal-01853459, version 1
  • ARXIV : 1808.01770


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



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