Paper 256-26 Fitting Generalized Additive Models with the GAM Procedure

This paper describes the use of the GAM proce-dure for fitting generalized additive models (Hastie and Tibshirani, 1990). PROC GAM, production in Re-lease 8.2, provides an array of powerful tools for data analysis, incorporating nonparametric regression and smoothing techniques as well as generalized distribu-tional modeling. Compared with other regression pro-cedures such as PROC REG and PROC GLM, the methodology behind PROC GAM relaxes the usual assumption of linearity, enabling you to uncover hid-den structure in the relationship between the indepen-dent variables and the dependent variable. Moreover, you can use PROC GAM to model not only Gaussian data, but also data from binary, Poisson, and other non-Gaussian distributions