Penalized contrast estimator for adaptive density deconvolution
| dc.creator | Comte, Fabienne | |
| dc.creator | Rozenholc, Yves | |
| dc.creator | Taupin, Marie-Luce | |
| dc.date | 2006-01-05 | |
| dc.date.accessioned | 2026-07-07T09:19:40Z | |
| dc.date.available | 2026-07-07T09:19:40Z | |
| dc.description | The authors consider the problem of estimating the density $g$ of independent and identically distributed variables $X\_i$, from a sample $Z\_1, ..., Z\_n$ where $Z\_i=X\_i+σε\_i$, $i=1, ..., n$, $ε$ is a noise independent of $X$, with $σε$ having known distribution. They present a model selection procedure allowing to construct an adaptive estimator of $g$ and to find non-asymptotic bounds for its $\mathbb{L}\_2(\mathbb{R})$-risk. The estimator achieves the minimax rate of convergence, in most cases where lowers bounds are available. A simulation study gives an illustration of the good practical performances of the method. | |
| dc.identifier | https://arxiv.org/abs/math/0601091 | |
| dc.identifier | http://arxiv.org/abs/math/0601091 | |
| dc.identifier | The Canadian Journal of Statistics / La Revue Canadienne de Statistique 34, 3 (2006) 431-452 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/154475 | |
| dc.subject | Statistics Theory | |
| dc.subject | Primary 62G07. Secondary 62G20 | |
| dc.title | Penalized contrast estimator for adaptive density deconvolution | |
| dc.type | text |