Metodi di regressione non parametrica additiva e il caso "Projection Pursuit Regression"

Abstract

This article presents a review of recent non parametric additive regression techniques that are appropriate for high dimensional data sets. These methods are related by the fact that they all use some type of additive approximation to describe the underlying regression model. The estimation of the regression function is obtained iteratively through backfitting procedures and the use of smoothers. The second part illustrate the Projection Pursuit Regression method by applying it to various data sets. Sample reuse techniques like bootstrap are used in order to study the particular problem of inference.