Schwarz Method for Penalized Quasi-Likelihood in Generalized Additive Models

Abstract

The topic is penalized quasi-maximum likelihood estimation in generalized additive models by an approximation using a sequence of sub-models, here called blocks. The Schwarz method uses a sequence of sub-models, The technique might be useful to model comparison, where the fitted values from a sub-model are used as starting values for a larger model. We show that the algorithm method converges in canonical link of generalized additive models, and a theorem about bound condition of sub-models convergence with uncanonical link.