Least angle and $\ell_1$ penalized regression: A review
Methodology
2008-05-21 v2 Machine Learning
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 (-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.
Cite
@article{arxiv.0802.0964,
title = {Least angle and $\ell_1$ penalized regression: A review},
author = {Tim Hesterberg and Nam Hee Choi and Lukas Meier and Chris Fraley},
journal= {arXiv preprint arXiv:0802.0964},
year = {2008}
}
Comments
Published in at http://dx.doi.org/10.1214/08-SS035 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org)