Automatic knot selection in smooth additive models

N. Carrizosa, M. Durbán, V. Guerrero

B-Spline regression is one of the most powerful and flexible methods in nonlinear regression. This methodology requires deciding the number and placement of changepoints - known as knots - before the estimation process. Traditionally, this has been done manually or addressed using a penalty term to control the regressor’s smoothness (P-Splines). In this work, the algorithm for automatic knot selection (A-splines) proposed in Goepp et al. (2025) for univariate regression with a continuous response variable is extended to the case of generalized additive models. Our approach is successfully tested in both synthetic and real datasets.

Keywords: B-splines, Generalized additive models, Knot selection

Scheduled

AMC4 Prediction and Classification
June 11, 2025 10:30 AM
MR 1

Other papers in the same session

Regresión de Mínimos Cuadrados Parciales para datos binarios

L. Vicente Gonzalez, E. Frutos Bernal, J. L. Vicente Villardón

Robust clustering for large datasets of weighted and mixed type data. An application to mental well-being in older adults.

F. Scielzo Ortiz, A. Grané, I. Albarrán

Solving the Rank Pricing Problem using a heuristic approach

A. Jiménez-Cordero, S. Pineda, J. M. Morales González

Testing a large number of populations using Gini covariance

M. D. Jiménez Gamero, M. R. Sillero Denamiel

Automatic knot selection in smooth additive models

Other papers in the same session

Cookie policy