The p-spectrum of Random Wavelet Series

The goal of multifractal analysis is to characterize the variations in local
regularity of functions or signals by computing the Hausdorff dimension of the
sets of points that share the same regularity. While classical approaches rely
on H\"older exponents and are limited to locally bounded functions, the notion
of $p$-exponents extends multifractal analysis to functions locally in $L^p$,
allowing a rigorous characterization of singularities in more general settings.
In this work, we propose a wavelet-based methodology to estimate the
$p$-spectrum from the distribution of wavelet coefficients across scales.
First, we establish an upper bound for the $p$-spectrum in terms of this
distribution, generalizing the classical H\"older case. The sharpness of this
bound is demonstrated for \textit{Random Wavelet Series}, showing that it can
be attained for a broad class of admissible distributions of wavelet
coefficients. Finally, within the class of functions sharing a prescribed
wavelet statistic, we prove that this upper bound is realized by a prevalent
set of functions, highlighting both its theoretical optimality and its
representativity of the typical multifractal behaviour in constrained function
spaces.