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
Keywords
SmoothingComputer scienceEconometricsStatisticsMathematics
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 31
- Issue
- 1
- Pages
- 3-21
- Citations
- 410
- Access
- Closed
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Cite This
Jerome H. Friedman,
Bernard W. Silverman
(1989).
Flexible Parsimonious Smoothing and Additive Modeling.
Technometrics
, 31
(1)
, 3-21.
https://doi.org/10.1080/00401706.1989.10488470
Identifiers
- DOI
- 10.1080/00401706.1989.10488470