Estimation of Gaussian graphs by model selection
| dc.creator | Giraud, Christophe | |
| dc.date | 2007-10-10 | |
| dc.date | 2008-07-16 | |
| dc.date.accessioned | 2026-07-07T09:50:18Z | |
| dc.date.available | 2026-07-07T09:50:18Z | |
| dc.description | We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from a n-sample of a Gaussian law P_C in R^p and focus on the disadvantageous case where n is smaller than p. To estimate the graph of conditional dependences of P_C, we introduce a collection of candidate graphs and then select one of them by minimizing a penalized empirical risk. Our main result assess the performance of the procedure in a non-asymptotic setting. We pay a special attention to the maximal degree D of the graphs that we can handle, which turns to be roughly n/(2 log p). | |
| dc.description | Published in at http://dx.doi.org/10.1214/08-EJS228 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0710.2044 | |
| dc.identifier | http://arxiv.org/abs/0710.2044 | |
| dc.identifier | Electronic Journal of Statistics 2 (2008) 542--563 | |
| dc.identifier | doi:10.1214/08-EJS228 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/164896 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G08 (Primary) 15A52, 62J05 (Secondary) | |
| dc.title | Estimation of Gaussian graphs by model selection | |
| dc.type | text |