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.


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.

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