Nonlinear multiple regression methods: a survey and extensions

Abstract

Abstract This paper reviews some nonlinear statistical procedures useful in function approximation, classification, regression and time‐series analysis. Primary emphasis is on piecewise linear models such as multivariate adaptive regression splines, adaptive logic networks, hinging hyperplanes and their conceptual differences. Potential and actual applications of these methods are cited. Software for implementation is discussed, and practical suggestions are given for improvement. Examples show the relative capabilities of the various methods, including their ability for universal approximation. Copyright © 2010 John Wiley & Sons, Ltd.