Fitting Generalized Additive Models with the GAM Procedure
نویسنده
چکیده
This paper describes the use of the GAM procedure for fitting generalized additive models (Hastie and Tibshirani, 1990). PROC GAM, production in Release 8.2, provides an array of powerful tools for data analysis, incorporating nonparametric regression and smoothing techniques as well as generalized distributional modeling. Compared with other regression procedures such as PROC REG and PROC GLM, the methodology behind PROC GAM relaxes the usual assumption of linearity, enabling you to uncover hidden structure in the relationship between the independent 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.
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