Fitting Generalized Additive Models with the GAM Procedure in SAS 9.2

Generalized additive models are useful in finding predictor-response relationships in many kinds of data without using a specific model. They combine the ability to explore many nonparametric relationships simultaneously with the distributional flexibility of generalized linear models. The approach often brings to light nonlinear dependency structures in your data. This paper discusses an example of fitting generalized additive models with the GAM procedure, which provides multiple types of smoothers with automatic selection of smoothing parameters. This paper uses the ODS Statistical Graphics to produce plots of integrated additive and smoothing components.