On the Peaking Phenomenon of the Lasso in Model Selection
| dc.creator | Kraemer, Nicole | |
| dc.date | 2009-04-28 | |
| dc.date.accessioned | 2026-07-07T13:09:27Z | |
| dc.date.available | 2026-07-07T13:09:27Z | |
| dc.description | I briefly report on some unexpected results that I obtained when optimizing the model parameters of the Lasso. In simulations with varying observations-to-variables ratio n=p, I typically observe a strong peak in the test error curve at the transition point n/p = 1. This peaking phenomenon is well-documented in scenarios that involve the inversion of the sample covariance matrix, and as I illustrate in this note, it is also the source of the peak for the Lasso. The key problem is the parametrization of the Lasso penalty (as e.g. in the current R package lars) and I present a solution in terms of a normalized Lasso parameter. | |
| dc.identifier | https://arxiv.org/abs/0904.4416 | |
| dc.identifier | http://arxiv.org/abs/0904.4416 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/228738 | |
| dc.subject | Methodology | |
| dc.title | On the Peaking Phenomenon of the Lasso in Model Selection | |
| dc.type | text |