Nonnegative Garrote Component Selection in Functional ANOVA models

Ming Yuan
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:660-666, 2007.

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 this Paper

BibTeX
@InProceedings{pmlr-v2-yuan07b, title = {Nonnegative Garrote Component Selection in Functional ANOVA models}, author = {Yuan, Ming}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {660--666}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/yuan07b/yuan07b.pdf}, url = {https://proceedings.mlr.press/v2/yuan07b.html}, 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.} }
Endnote
%0 Conference Paper %T Nonnegative Garrote Component Selection in Functional ANOVA models %A Ming Yuan %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-yuan07b %I PMLR %P 660--666 %U https://proceedings.mlr.press/v2/yuan07b.html %V 2 %X 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.
RIS
TY - CPAPER TI - Nonnegative Garrote Component Selection in Functional ANOVA models AU - Ming Yuan BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-yuan07b PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 660 EP - 666 L1 - http://proceedings.mlr.press/v2/yuan07b/yuan07b.pdf UR - https://proceedings.mlr.press/v2/yuan07b.html AB - 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. ER -
APA
Yuan, M.. (2007). Nonnegative Garrote Component Selection in Functional ANOVA models. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:660-666 Available from https://proceedings.mlr.press/v2/yuan07b.html.

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