Regularization Methods for Additive Models
This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform variable selection. However, these procedures are inefficient or computationally expensive in high dimension. To answer this problem, the lasso technique has been adapted to additive models, but its experimental performance has not been analyzed.