  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.
