Open Access
March, 1991 Multivariate Adaptive Regression Splines
Jerome H. Friedman
Ann. Statist. 19(1): 1-67 (March, 1991). DOI: 10.1214/aos/1176347963


A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however, this method produces continuous models with continuous derivatives. It has more power and flexibility to model relationships that are nearly additive or involve interactions in at most a few variables. In addition, the model can be represented in a form that separately identifies the additive contributions and those associated with the different multivariable interactions.


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Jerome H. Friedman. "Multivariate Adaptive Regression Splines." Ann. Statist. 19 (1) 1 - 67, March, 1991.


Published: March, 1991
First available in Project Euclid: 12 April 2007

zbMATH: 0765.62064
MathSciNet: MR1091842
Digital Object Identifier: 10.1214/aos/1176347963

Primary: 62J02
Secondary: 62H30 , 65D07 , 65D10 , 65D15 , 68T05 , 68T10 , 90A19 , 93C35 , 93E11 , 93E14

Keywords: AID , CART , multivariable function approximation , multivariate smoothing , Nonparametric multiple regression , recursive partitioning , splines , statistical learning neural networks

Rights: Copyright © 1991 Institute of Mathematical Statistics

Vol.19 • No. 1 • March, 1991
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