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2008 Least angle and 1 penalized regression: A review
Tim Hesterberg, Nam Hee Choi, Lukas Meier, Chris Fraley
Statist. Surv. 2: 61-93 (2008). DOI: 10.1214/08-SS035

Abstract

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (1-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, we give an overview of Least Angle Regression and the current state of related research.

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Tim Hesterberg. Nam Hee Choi. Lukas Meier. Chris Fraley. "Least angle and 1 penalized regression: A review." Statist. Surv. 2 61 - 93, 2008. https://doi.org/10.1214/08-SS035

Information

Published: 2008
First available in Project Euclid: 20 May 2008

zbMATH: 1189.62070
MathSciNet: MR2520981
Digital Object Identifier: 10.1214/08-SS035

Subjects:
Primary: 62J07
Secondary: 69J99

Rights: Copyright © 2008 The author, under a Creative Commons Attribution License

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