Data Analysis Papers
Fitting Generalized Additive Models with the GAM Procedure in SAS® 9.2
Cai, Weijie; SAS Institute, 2008
Abstract
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.
