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.