Nonlinear Regression Models

Abstract

Chapter 6 discussed regression models that were intrinsically linear. In this chapter we present regression models that are inherently nonlinear in nature. When using these models, the exact form of the nonlinearity does not need to be known explicitly or specified prior to model training. These models include neural networks (Section 7.1), multivariate adaptive regression splines (Section 7.2), support vector machines (Section 7.3), and K-nearest neighbors (Section 7.4). In the Computing Section (7.5) we demonstrate how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.