Flexible Parsimonious Smoothing and Additive Modeling

1989414 citationsJournal Article

Authors

Jerome H. Friedman · SLAC National Accelerator Laboratory

Bernard W. Silverman · Department of Mathematical Sciences

Abstract

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. KEY WORDS: Generalized cross-validationKnot positionPiecewise linearRegression analysis

Topics & Keywords

Computer Graphics and Visualization Techniques 3D Shape Modeling and Analysis Data Visualization and Analytics

Publication Details

Published in: Technometrics

Volume 31, Issue 1, pp. 3-21

DOI: 10.1080/00401706.1989.10488470

Field-Weighted Citation Impact: 5.69

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Flexible Parsimonious Smoothing and Additive Modeling

Authors

Abstract

Topics & Keywords

Publication Details