Work

Flexible Parsimonious Smoothing and Additive Modeling

Year: 1989

Type: 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.

Source: Technometrics

DOI: 10.1080/00401706.1989.10488470

Authors: Jerome H. Friedman, Bernard W. Silverman

Institutions: SLAC National Accelerator Laboratory, Stanford University, University of Bath

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: U.S. Department of Energy

Awards: G1325202077, G2371548206