Article first published online: 16 JUL 2010
Copyright © 2010 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 2, Issue 5, pages 590–599, September/October 2010
How to Cite
Gijbels, I. and Prosdocimi, I. (2010), Loess. WIREs Comp Stat, 2: 590–599. doi: 10.1002/wics.104
- Issue published online: 8 SEP 2010
- Article first published online: 16 JUL 2010
- local modeling;
- least squares regression;
- local polynomial fit;
- bandwidth parameter
Linear least squares regression is among the most well known classical methods. This and other parametric least squares regression models do not perform well when the modeling is too restrictive to capture the nonlinear effect the covariates have on the response. Locally weighted least squares regression (loess) is a modern technique that combines much of the simplicity of the classical least squares method with the flexibility of nonlinear regression. The basic idea behind the method is to model a regression function only locally as having a specific form. This paper discusses the method in the univariate and multivariate case and robustifications of the technique, and provides illustrative examples. Copyright © 2010 John Wiley & Sons, Inc.
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