An efficient approximation of L 2 Boosting with component-wise smoothing splines is considered. Smoothing spline base-learners are replaced by P-spline base-learners, which yield similar prediction errors but are more advantageous from a computational point of view. A detailed analysis of the effect of various P-spline hyper-parameters on the boosting fit is given. In addition, a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates is derived.
%0 Journal Article
%1 schmid_boosting_2008
%A Schmid, Matthias
%A Hothorn, Torsten
%D 2008
%J Computational Statistics & Data Analysis
%K imported
%N 2
%P 298--311
%R 10.1016/j.csda.2008.09.009
%T Boosting additive models using component-wise P-Splines
%U http://www.sciencedirect.com/science/article/pii/S0167947308004416
%V 53
%X An efficient approximation of L 2 Boosting with component-wise smoothing splines is considered. Smoothing spline base-learners are replaced by P-spline base-learners, which yield similar prediction errors but are more advantageous from a computational point of view. A detailed analysis of the effect of various P-spline hyper-parameters on the boosting fit is given. In addition, a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates is derived.
@article{schmid_boosting_2008,
abstract = {An efficient approximation of L 2 Boosting with component-wise smoothing splines is considered. Smoothing spline base-learners are replaced by P-spline base-learners, which yield similar prediction errors but are more advantageous from a computational point of view. A detailed analysis of the effect of various P-spline hyper-parameters on the boosting fit is given. In addition, a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates is derived.},
added-at = {2017-01-09T13:57:26.000+0100},
author = {Schmid, Matthias and Hothorn, Torsten},
biburl = {https://www.bibsonomy.org/bibtex/293c74761e08b7c9b55f9f08800ab0bc4/yourwelcome},
doi = {10.1016/j.csda.2008.09.009},
interhash = {fd337f0f0521fd8c10f530dfacb7492b},
intrahash = {93c74761e08b7c9b55f9f08800ab0bc4},
issn = {0167-9473},
journal = {Computational Statistics \& Data Analysis},
keywords = {imported},
month = dec,
number = 2,
pages = {298--311},
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Boosting additive models using component-wise {P}-{Splines}},
url = {http://www.sciencedirect.com/science/article/pii/S0167947308004416},
urldate = {2013-06-04},
volume = 53,
year = 2008
}