SpAM: Sparse Additive Models

Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman

Abstract: We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We de- rive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties of SpAM is given together with empirical results on synthetic and real data, show- ing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.