 Spline regression with automatic knot selectionVivien Goepp, Olivier Bouaziz and Grégory Nuel Computational Statistics & Data Analysis, 2025, vol. 202, issue C Abstract: Spline regression has proven to be a useful tool for nonparametric regression. The flexibility of this function family is based on basepoints defining shifts in the behavior of the function – called knots. The question of setting the adequate number of knots and their placement is usually overcome by penalizing over the spline's overall smoothness (e.g. P-splines). However, there are areas of application where finding the best knot placement is of interest. A new method is introduced for automatically selecting knots in spline regression. The approach consists in setting many initial knots and fitting the spline regression through a penalized likelihood procedure called adaptive ridge, which discards the least relevant knots. The method – called A-splines, for adaptive splines – compares favorably with other knot selection methods: it runs way faster (∼10 to ∼400 faster) than comparable methods and has close to equal predictive performance. A-splines are applied to both simulated and real datasets. Keywords: Spline regression; Adaptive ridge; B-splines; Changepoint detection; Penalized likelihood (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:202:y:2025:i:c:s0167947324001270 DOI: 10.1016/j.csda.2024.108043 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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