Generalized Additive Models

Generalized Additive Models are generalizations of generalized linear models. In generalized linear models, the transformed dependent variable values are predicted from (is linked to) a linear combination of predictor variables; the transformation is referred to as the link function; also, different distributions can be assumed for the dependent variable values. An example of a generalized linear model is the Logit Regression model, where the dependent variable is assumed to be binomial, and the link function is the logit transformation. In generalized additive models, the linear function of the predictor values is replaced by an unspecified (non-parametric) function, obtained by applying a scatterplot smoother to the scatterplot of partial residuals (for the transformed dependent variable values).  

For an overview of generalized additive models see the Introductory Overview for the Generalized Additive Models (GAM) module. See also, Hastie and Tibshirani, 1990, or Schimek, 2000.