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1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

Abstract : This paper is devoted to the estimation of an unknown function $f$ in the framework of a Gaussian white noise model. The noise process is represented by $t ightarrow\frac{1}{\sqrt{n}}\int {0}^{t}gx dB x$, where the variance function $g$ is assumed to be known. Adopting the maxiset point of view, we study the performance of two different hard thresholding estimators in $\mathbb{L}^p$ norm. In a first part, we expand $f$ on a compactly supported wavelet basis $\{\psi {\lambda}.; \ \lambda\in\Lambda\}$. From this decomposition, we use some results about the heteroscedastic white noise model to construct a well adapted hard thresholding estimator and to exhibit the associated maxiset. In a second part, we introduce the classes of Muckenhoupt weights and we use this analytical tools to investigate the geometrical properties of warped wavelet basis $\{\psi {\lambda}T.; \ \lambda\in\Lambda\}$ in $\mathbb{L}^p$ norm. Expanding $f$ on such a basis and considering the associated hard thresholding estimator, we investigate the maxiset properties under some assumptions on $g$. We finally apply this result to find an upper bound over weighted Besov spaces.

Keywords : Non parametric estimation maximal spaces thresholding rules Besov spaces weak Besov spaces weighted Besov spaces Gaussian white noise model Muckenhoupt weights warped basis





Autor: Christophe Chesneau -

Fuente: https://hal.archives-ouvertes.fr/



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