Genetic algorithms for the selection of smoothing parameters in additive models

Abstract

Additive models of the type y=f 1(x 1)+...+f p(x p)+? where f j , j=1,..,p, have unspecified functional form, are flexible statistical regression models which can be used to characterize nonlinear regression effects. One way of fitting additive models is the expansion in B-splines combined with penalization which prevents overfitting. The performance of this penalized B-spline (called P-spline) approach strongly depends on the choice of the amount of smoothing used for components f j . In particular for higher dimensional settings this is a computationaly demanding task. In this paper we treat the problem of choosing the smoothing parameters for P-splines by genetic algorithms. In several simulation studies this approach is compared to various alternative methods of fitting additive models. In particular functions with different spatial variability are considered and the effect of constant respectively local adaptive smoothing parameters is evaluated.