Finite sample penalization in 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 | We consider the problem of estimating the density $g$ of identically distributed variables $X\_i$, from a sample $Z\_1, ..., Z\_n$ where $Z\_i=X\_i+σε\_i$, $i=1, ..., n$ and $σε\_i$ is a noise independent of $X\_i$ with known density $ σ^{-1}f\_ε(./σ)$. We generalize adaptive estimators, constructed by a model selection procedure, described in Comte et al. (2005). We study numerically their properties in various contexts and we test their robustness. Comparisons are made with respect to deconvolution kernel estimators, misspecification of errors, dependency,... It appears that our estimation algorithm, based on a fast procedure, performs very well in all contexts. | |
| dc.identifier | https://arxiv.org/abs/math/0601098 | |
| dc.identifier | http://arxiv.org/abs/math/0601098 | |
| dc.identifier | Journal of Statistical Computation and Simulation 77, 11 (2007) 977-1000 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/154476 | |
| dc.subject | Statistics Theory | |
| dc.subject | Primary 62G07. Secondary 62G20 | |
| dc.title | Finite sample penalization in adaptive density deconvolution | |
| dc.type | text |