Sparse estimation of large covariance matrices via a nested Lasso penalty
| dc.creator | Levina, Elizaveta | |
| dc.creator | Rothman, Adam | |
| dc.creator | Zhu, Ji | |
| dc.date | 2008-03-27 | |
| dc.date.accessioned | 2026-07-07T12:17:53Z | |
| dc.date.available | 2026-07-07T12:17:53Z | |
| dc.description | The paper proposes a new covariance estimator for large covariance matrices when the variables have a natural ordering. Using the Cholesky decomposition of the inverse, we impose a banded structure on the Cholesky factor, and select the bandwidth adaptively for each row of the Cholesky factor, using a novel penalty we call nested Lasso. This structure has more flexibility than regular banding, but, unlike regular Lasso applied to the entries of the Cholesky factor, results in a sparse estimator for the inverse of the covariance matrix. An iterative algorithm for solving the optimization problem is developed. The estimator is compared to a number of other covariance estimators and is shown to do best, both in simulations and on a real data example. Simulations show that the margin by which the estimator outperforms its competitors tends to increase with dimension. | |
| dc.description | Published in at http://dx.doi.org/10.1214/07-AOAS139 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0803.3872 | |
| dc.identifier | http://arxiv.org/abs/0803.3872 | |
| dc.identifier | Annals of Applied Statistics 2008, Vol. 2, No. 1, 245-263 | |
| dc.identifier | doi:10.1214/07-AOAS139 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/212228 | |
| dc.subject | Applications | |
| dc.title | Sparse estimation of large covariance matrices via a nested Lasso penalty | |
| dc.type | text |