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. We propose a modified lasso for additive models, performing variable selection. A benchmark is developed to examine its practical behavior, comparing it with forward selection. Our simulation studies suggest ability to carry out model selection of the proposed method. The lasso technique shows up better than forward selection in the most complex situations. The computing time of modified lasso is considerably smaller since it does not depend on the number of relevant variables.