Simultaneous Confidence Corridors and Variable Selection for Generalized Additive Models

Abstract

In spite of the widespread use of generalized additive models (GAMs), there is no well established methodology for simultaneous inference and variable selection for the components of GAM. There is no doubt that both, inference on the marginal component functions and their selection, are essential in this additive statistical models. To this end, we establish simultaneous confidence corridors (SCCs) and a variable selection criteria through the spline-backfitted kernel smoothing techniques. To characterize the global features of each component, SCCs are constructed for testing their shapes. By extending the BIC to additive models with identity/trivial link, an asymptotically consistent BIC approach for variable selection is proposed. Our procedures are examined in simulations for its theoretical accuracy and performance, and used to forecast the default probability of listed Japanese companies.