Generalized Additive Models

Regression models play an important role in many applied settings, by enabling predictive analysis, revealing classification rules, and providing data-analytic tools for understanding the interactions between different variables. Although attractively simple, traditional linear regression often fails in practical situations because real-life effects are usually nonlinear. This article describes statistical methods that may be used to identify and characterize general nonlinear regressions, without requiring the analyst to prespecify the form of the nonlinear relationship. These methods form the basis of the generalized additive models approach to data analysis. The choice of the link function g permits the…