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Technometrics Volume 31, 1989 - Issue 1
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Original Articles

Flexible Parsimonious Smoothing and Additive Modeling

Jerome H. Friedman Department of Statistics and Stanford Linear Accelerator Center, Stanford University, Stanford, CA, 94305
&
Bernard W. Silverman School of Mathematical Sciences University of Bath, Bath, BA2 7AY, United Kingdom
Pages 3-21 | Published online: 23 Mar 2012
 
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Abstract

A simple method is presented for fitting regression models that are nonlinear in the explanatory variables. Despite its simplicity—or perhaps because of it—the method has some powerful characteristics that cause it to be competitive with and often superior to more sophisticated techniques, especially for small data sets in the presence of high noise.

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