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Article
Wavelet eigenvalue regression in high dimensions
In this paper, we construct the wavelet eigenvalue regression methodology (Abry and Didier in J Multivar Anal 168:75–104, 2018a; in Bernoulli 24(2):895–928, 2018b) in high dimensions. We assume that possibly n...
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Chapter and Conference Paper
New Exponents for Pointwise Singularity Classification
We introduce new tools for pointwise singularity classification: We investigate the properties of the two-variable function which is defined at every point as the p-exponent of a fractional integral of order t; n...
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Chapter and Conference Paper
Multifractal Analysis Based on p-Exponents and Lacunarity Exponents
Many examples of signals and images cannot be modeled by locally bounded functions, so that the standard multifractal analysis, based on the Hölder exponent, is not feasible. We present a multifractal analysis...
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Chapter
On the impact of the number of vanishing moments on the dependence structures of compound Poisson motion and fractional Brownian motion in multifractal time
From a theoretical perspective, scale invariance, or simply scaling, can fruitfully be modeled with classes of multifractal stochastic processes, designed from positive multiplicative martingales (or cascades)...
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Article
Second order properties of distribution tails and estimation of tail exponents in random difference equations
According to a celebrated result of Kesten (Acta Math 131:207–248, 1973), random difference equations have a power-law distribution tail in the asymptotic sense. Empirical evidence shows that classical estimators...
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Chapter and Conference Paper
Wavelet Decomposition of Measures: Application to Multifractal Analysis of Images
We show the relevance of multifractal analysis for some problems in image processing. We relate it to the standard question of the determination of correct function space settings. We show why a scale-invarian...