Adaptive goodness-of-fit testing from ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Adaptive goodness-of-fit testing from indirect observations
Auteur(s) :
Butucea, Cristina [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Matias, Catherine [Auteur correspondant]
Laboratoire Statistique et Génome [LSG]
Pouet, Christophe [Auteur]
Laboratoire d'Analyse, Topologie, Probabilités [LATP]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Matias, Catherine [Auteur correspondant]
Laboratoire Statistique et Génome [LSG]
Pouet, Christophe [Auteur]
Laboratoire d'Analyse, Topologie, Probabilités [LATP]
Titre de la revue :
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques
Pagination :
352-372
Éditeur :
Institut Henri Poincaré (IHP)
Date de publication :
2009
ISSN :
0246-0203
Mot(s)-clé(s) en anglais :
Adaptive nonparametric tests
Convolution model
Goodness-of-fit tests
Infinitely differentiable functions
Partially known noise
Quadratic functional estimation
Sobolev classes
Stable laws
Convolution model
Goodness-of-fit tests
Infinitely differentiable functions
Partially known noise
Quadratic functional estimation
Sobolev classes
Stable laws
Discipline(s) HAL :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
Résumé en anglais : [en]
In a convolution model, we observe random variables whose distribution is the convolution of some unknown density $f$ and some known noise density $g$. We assume that $g$ is polynomially smooth. We provide goodness-of-fit ...
Lire la suite >In a convolution model, we observe random variables whose distribution is the convolution of some unknown density $f$ and some known noise density $g$. We assume that $g$ is polynomially smooth. We provide goodness-of-fit testing procedures for the test $H_0:f=f_0$, where the alternative $H_1$ is expressed with respect to $\mathbb{L}_2$-norm (\emph{i.e.} has the form $\psi_{n}^{-2}\|f-f_0\|_2^2 \ge \mathcal{C}$). Our procedure is adaptive with respect to the unknown smoothness parameter $\tau$ of $f$. Different testing rates ($\psi_n$) are obtained according to whether $f_0$ is polynomially or exponentially smooth. A price for adaptation is noted and for computing this, we provide a non-uniform Berry-Esseen type theorem for degenerate $U$-statistics. In the case of polynomially smooth $f_0$, we prove that the price for adaptation is optimal. We emphasise the fact that the alternative may contain functions smoother than the null density to be tested, which is new in the context of goodness-of-fit tests.Lire moins >
Lire la suite >In a convolution model, we observe random variables whose distribution is the convolution of some unknown density $f$ and some known noise density $g$. We assume that $g$ is polynomially smooth. We provide goodness-of-fit testing procedures for the test $H_0:f=f_0$, where the alternative $H_1$ is expressed with respect to $\mathbb{L}_2$-norm (\emph{i.e.} has the form $\psi_{n}^{-2}\|f-f_0\|_2^2 \ge \mathcal{C}$). Our procedure is adaptive with respect to the unknown smoothness parameter $\tau$ of $f$. Different testing rates ($\psi_n$) are obtained according to whether $f_0$ is polynomially or exponentially smooth. A price for adaptation is noted and for computing this, we provide a non-uniform Berry-Esseen type theorem for degenerate $U$-statistics. In the case of polynomially smooth $f_0$, we prove that the price for adaptation is optimal. We emphasise the fact that the alternative may contain functions smoother than the null density to be tested, which is new in the context of goodness-of-fit tests.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- document
- Accès libre
- Accéder au document
- BMP_final.pdf
- Accès libre
- Accéder au document
- 08-aihp166
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- BMP_final.pdf
- Accès libre
- Accéder au document