The development of wavelet theory has in recent years spawned applications in signal
processing, in fast algorithms for integral transforms, and in image and function …

Danger lies not in what we don't know, but in what we think we know that just ain't so. Mark
Twain (1835–1910) The use of wavelets in statistics has received wide attention in the last …

This paper deals with the problem of multivariate copula density estimation. Using wavelet
methods we provide two shrinkage procedures based on thresholding rules for which …

Cette thèse se divise en deux parties indépendantes. Dans la première, nous considérons
un modèle microscopique individu-centré pour décrire une population structurée par traits et …

We propose a novel approach to filter bank learning for time-series by considering spectral
decompositions of signals defined as a Group Transform. This framework allows us to …

Koopman theory studies dynamical systems in terms of operator theoretic properties of the
Perron-Frobenius operator $\mathcal {P} $ and Koopman operator $\mathcal {U} …

Adaptive estimation under single-index constraint in a regression model Page 1 The Annals
of Statistics 2014, Vol. 42, No. 1, 1–28 DOI: 10.1214/13-AOS1152 © Institute of …

We investigate function estimation in nonparametric regression models with random design
and heteroscedastic correlated noise. Adaptive properties of warped wavelet nonlinear …

Based on the notion of first order dyadic p-variation, we give a new characterization of Besov
spaces Bp, qs ([0, 1]) for 0< s< 1, 1≤ p, q≤+∞ and s> 1/p. We also give results in the case …

Nonparametric estimation of a quantile density function by wavelet methods - ScienceDirect
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