Journal of the Royal Statistical Society B, Statistical Methodology vol:68 issue:3 pages:383-409
We formulate and solve a semiparametric fitting problem with regularization constraints. The model that we focus on is composed of a parametric non-linear part and a nonparametric part that can be reconstructed via splines. Regularization is employed to impose a certain degree of smoothness on the nonparametric part. Semiparametric regression is presented as a generalization of non-linear regression, and all important differences that arise from the statistical and computational points of view are highlighted. We motivate the problem formulation with a biomedical signal processing application.