Least angle and $\ell_1$ penalized regression: A review
| dc.creator | Hesterberg, Tim | |
| dc.creator | Choi, Nam Hee | |
| dc.creator | Meier, Lukas | |
| dc.creator | Fraley, Chris | |
| dc.date | 2008-02-07 | |
| dc.date | 2008-05-21 | |
| dc.date.accessioned | 2026-07-07T09:39:48Z | |
| dc.date.available | 2026-07-07T09:39:48Z | |
| dc.description | 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 ($\ell_1$-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. | |
| dc.description | 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) | |
| dc.identifier | https://arxiv.org/abs/0802.0964 | |
| dc.identifier | http://arxiv.org/abs/0802.0964 | |
| dc.identifier | Statistics Surveys 2008, Vol. 2, 61-93 | |
| dc.identifier | doi:10.1214/08-SS035 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/161306 | |
| dc.subject | Methodology | |
| dc.subject | Machine Learning | |
| dc.subject | 62J07 (Primary) 69J99 (Secondary) | |
| dc.title | Least angle and $\ell_1$ penalized regression: A review | |
| dc.type | text |