Multivariate Adaptive Regression Splines

TLDR

The method builds on recursive partitioning for regression, inheriting its attractive properties. The study introduces a flexible regression method for high‑dimensional data. The method uses an automatically tuned expansion of product spline basis functions to model data. The resulting models are continuous with continuous derivatives, more powerful and flexible than recursive partitioning, and can separate additive and interaction effects.

Abstract

A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however, this method produces continuous models with continuous derivatives. It has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables. In addition, the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions.