Automatic structure recovery for generalized additive models

In this article, we propose an automatic structure recovery method for generalized additive models (GAMs) by extending Wu and Stefanski's approach. In a similar vein, the proposed method is based on a local scoring algorithm coupled with local polynomial smoothing, along with a kernel-based variable selection approach. Given a specific degree M, the goal is to identify predictors contributing polynomially at different degrees up to M and predictors that contribute beyond degree M. By focusing on two GAMs, logistic regression and Poisson regression, we illustrate the performance of the proposed method using Monte Carlo simulation studies and two real data examples.