Sparse Additive Models

Understand

We present a new class of methods 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 derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size.
• SpAM is closely related to the COSSO model of Lin and Zhang (2006), but decouples smoothing and sparsity, enabling the use of arbitrary nonparametric smoothers.