Automatic knot selection in smooth additive models
B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the number and placement of changepoints, known as knots, prior to the estimation process. Such knot sequence determines the dimension of the B-spline basis used to represent the regression function and the number of coefficients to be estimated. Therefore, the knots' choice affects the model's flexibility, influencing its smoothness and goodness-of-fit. Traditionally, this problem has been addressed either by explicitly selecting knots, via knot-selection algorithms, or by regularization methods, such as P-splines, which automatically tune the regr...
| Authors | Nicolás Carrizosa, Vanesa Guerrero, María Durbán |
|---|---|
| Primary category | stat.ML |
| Published | 23 July 2026 |
| Last updated at source | 4 September 2026 |
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