High-dimensional additive modeling

Details High-dimensional additive modeling High-dimensional additive modeling

2008-06-25

arxiv.org/abs/0806.4115v4

Annals of Statistics 2009, Vol. 37, No. 6B, 3779-3821

Statistics

Machine Learning

Published in at http://dx.doi.org/10.1214/09-AOS692 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/09-AOS692

We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results which yield asymptotic optimality of our estimator for high dimensional but sparse additive models. Finally, an adaptive version of our sparsity-smoothness penalized approach yields large additional performance gains.

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