Knot selection by boosting techniques

A novel concept for estimating smooth functions by selection
techniques based on boosting is developed. It is suggested to put
radial basis functions with different spreads at each knot and to
do selection and estimation simultaneously by a componentwise
boosting algorithm. The methodology of various other smoothing and
knot selection procedures (e.g. stepwise selection) is summarized.
They are compared to the proposed approach by extensive simulations
for various unidimensional settings, including varying spatial
variation and heteroskedasticity, as well as on a real world data
example. Finally, an extension of the proposed method to surface
fitting is evaluated numerically on both, simulation and real data.
The proposed knot selection technique is shown to be a strong
competitor to existing methods for knot selection.