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Least angle and ℓ1 penalized regression: A review

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Author(s): Tim Hesterberg | Nam Hee Choi | Lukas Meier | Chris Fraley

Journal: Statistics Surveys
ISSN 1935-7516

Volume: 2;
Start page: 61;
Date: 2008;
Original page

Keywords: Lasso | Regression | Regularization | ℓ1 penalty | Variable selection

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 (ℓ1-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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