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.
Palabras clave: B-splines, Generalized additive models, Knot selection
Programado
AMC4 Predicción Clasificación
11 de junio de 2025 10:30
MR 1