Flexible Parsimonious Smoothing and Additive Modeling


Friedman, Jerome H.; Silverman, Bernard W.   (1989, ASQC and the American Statistical Association)   Stanford University, Stanford, CA; University of Bath, United Kingdom

Technometrics    Vol. 31    No. 1
QICID: 9377    February 1989    pp. 3-21
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Article 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.


Validation,Knot position,Linear models,Regression analysis

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