A Statistical view of Iterative Methods for Linear Inverse Problems
| dc.creator | Fermin, Ana K. | |
| dc.creator | Ludena, Carenne | |
| dc.date | 2005-04-04 | |
| dc.date.accessioned | 2026-07-07T08:06:48Z | |
| dc.date.available | 2026-07-07T08:06:48Z | |
| dc.description | In this article we study the problem of recovering the unknown solution of a linear ill-posed problem, via iterative regularization methods. We review the problem of projection-regularization from a statistical point of view. A basic purpose of the paper is the consideration of adaptive model selection for determining regularization parameters. This article introduces a new regularized estimator which has the best possible adaptive properties for a wide range of linear functionals. We derive non asymptotic upper bounds for the mean square error of the estimator and give the optimal convergence rates. | |
| dc.description | 21 pages | |
| dc.identifier | https://arxiv.org/abs/math/0504064 | |
| dc.identifier | http://arxiv.org/abs/math/0504064 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/130733 | |
| dc.subject | Statistics Theory | |
| dc.title | A Statistical view of Iterative Methods for Linear Inverse Problems | |
| dc.type | text |