Nonnegative Garrote Component Selection in Functional ANOVA Models
Abstract
We consider the problem of component selection in a functional ANOVA model. A nonparametric extension of the nonnegative garrote (Breiman, 1996) is proposed. We show that the whole solution path of the proposed method can be efficiently computed, which, in turn , facilitates the selection of the tuning parameter. We also show that the final estimate enjoys nice theoretical properties given that the tuning parameter is appropriately chosen. Simulation and a real data example demonstrate promising performance of the new approach.
Cite
Text
Yuan. "Nonnegative Garrote Component Selection in Functional ANOVA Models." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Yuan. "Nonnegative Garrote Component Selection in Functional ANOVA Models." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/yuan2007aistats-nonnegative/)BibTeX
@inproceedings{yuan2007aistats-nonnegative,
title = {{Nonnegative Garrote Component Selection in Functional ANOVA Models}},
author = {Yuan, Ming},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {660-666},
volume = {2},
url = {https://mlanthology.org/aistats/2007/yuan2007aistats-nonnegative/}
}