Smoothing parameter selection for smoothing splines: a simulation study

摘要

Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk estimation methods have never been reported in the literature. Empirical conclusions from sand recommendations based on the simulation results will be provided. One noteworthy empirical observation is that the popular method, generalized cross-validation, was outperformed by another method, an improved Akaike Information criterion, that shares the same assumptions and computational complexity.