Nonlinear Regression and Optimization

Jonathon D. Brown
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Jonathon D. Brown: University of Washington, Department of Psychology

Chapter Chapter 10 in Advanced Statistics for the Behavioral Sciences, 2018, pp 323-360 from Springer

Abstract: Abstract In Chap. 9 we learned how to model a nonlinear relation using a cubic spline; in this chapter we will learn how to model a nonlinear relation using nonlinear regression. Methods for performing nonlinear regression have been around for many years, but they were not commonly used before the ready accessibility of high-speed computers. Instead, nonlinear relations were transformed into linear ones, and the transformed data were analyzed using ordinary least squares (OLS) estimation. There were sound reasons for adopting this approach, as linear models are easier to analyze and interpret than are nonlinear models. At the same time, not all nonlinear relations can be linearized, and increases in computing power and the attendant refinement of a wide-variety of nonlinear models have led many researchers to abandon linearizing transformations in favor of nonlinear regression. For this reason, all contemporary software packages offer tools for performing nonlinear regression.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-93549-2_10

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DOI: 10.1007/978-3-319-93549-2_10

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