Nonlinear Regression Models

Max Kuhn and Kjell Johnson
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Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 7 in Applied Predictive Modeling, 2013, pp 141-171 from Springer

Abstract: 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.

Keywords: Support Vector Machine; Radial Basis Function; Linear Regression Model; Support Vector Regression; Hide Unit (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6849-3_7

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DOI: 10.1007/978-1-4614-6849-3_7

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