  We introduce GAMSEL (Generalized Additive Model Selection), a penalized
likelihood approach for fitting sparse generalized additive models in high
dimension. Our method interpolates between null, linear and additive models by
allowing the effect of each variable to be estimated as being either zero,
linear, or a low-complexity curve, as determined by the data. We present a
blockwise coordinate descent procedure for efficiently optimizing the penalized
likelihood objective over a dense grid of the tuning parameter, producing a
regularization path of additive models. We demonstrate the performance of our
method on both real and simulated data examples, and compare it with existing
techniques for additive model selection.
