Abstract:This paper is concerned with Mandelbrot's stochastic cascade measures. The problems of (i) scaling exponents of structure functions of the measure, τ(#), and (ii) multifractal dimensions are considered for cascades with a generator vector (wι w c ) of the general type. These problems were previously studied for independent strongly bounded variables w l : 0 < a < w/ ^ c. Consequently, a broad class of models used in applications, including Kolmogorov's log-normal model in turbulence, log-stable "universal" cas… Show more
“…In this section, we consider the random measures introduced by B. Mandelbrot in [24]. Up to now, their multifractal analysis has been mostly performed in the setting of the tree of c-adic intervals (see [22,21,14,30,2,3]). Nevertheless, Arbeiter and Patzschke [1] obtained a result in the same spirit as ours under strong assumptions without setting a general frame.…”
Abstract. Physicists usually compute dimensions by using boxes and they also do so when dealing with multifractals. Also in the study of some dynamical systems and multiplicative processes, boxes naturally appear. On the other hand, in geometric measure theory, it is preferred to perform computations which do not depend on a grid.This article provides a bridge between the boxes and the grid-free approaches to the multifractal analysis of measures. Results for quasi-Bernoulli measures and statistically self-similar measures are obtained.
“…In this section, we consider the random measures introduced by B. Mandelbrot in [24]. Up to now, their multifractal analysis has been mostly performed in the setting of the tree of c-adic intervals (see [22,21,14,30,2,3]). Nevertheless, Arbeiter and Patzschke [1] obtained a result in the same spirit as ours under strong assumptions without setting a general frame.…”
Abstract. Physicists usually compute dimensions by using boxes and they also do so when dealing with multifractals. Also in the study of some dynamical systems and multiplicative processes, boxes naturally appear. On the other hand, in geometric measure theory, it is preferred to perform computations which do not depend on a grid.This article provides a bridge between the boxes and the grid-free approaches to the multifractal analysis of measures. Results for quasi-Bernoulli measures and statistically self-similar measures are obtained.
“…As we said, multifractal analysis of µ [27,34,43,23,1,7,5] usually considers Hölder singularities sets of the form (1.1) and their Hausdorff dimension d µ (α), which is a measure of their size. The method used to compute d µ (α) is to find a random measure µ α (of the same nature as µ) such that µ α is concentrated on E µ α ∩ E µα τ * µ (α) .…”
Section: Growth Speed In µ I 'S Hölder Singularity Setsmentioning
confidence: 99%
“…Since E(W −h K ) < ∞, using the approach of [43] to study the behavior at ∞ of Laplace transforms satisfying an inequality like (4.6) (see also [4,38]…”
This paper investigates new properties concerning the multifractal structure of a class of random self-similar measures. These measures include the well-known
“…There is a huge mathematical literature devoted to the study of such a construction and we refer the reader to Refs. [20,21,22,23] for rigourous results about the existence, regularity and statistical properties of Mandelbrot cascades. In physics or other applied sciences, as recalled in the introduction, the previous construction (and many of its variants) is considered as the paradigm for multifractal objects and has been often used as a reference model in order to reproduce observed multiscaling.…”
Section: A Discrete Multiplicative Cascadesmentioning
We define a large class of continuous time multifractal random measures and processes with arbitrary log-infinitely divisible exact or asymptotic scaling law. These processes generalize within a unified framework both the recently defined log-normal Multifractal Random Walk (MRW) [1, 2] and the log-Poisson "product of cynlindrical pulses" [3]. Our construction is based on some "continuous stochastic multiplication" (as introduced in [4]) from coarse to fine scales that can be seen as a continuous interpolation of discrete multiplicative cascades. We prove the stochastic convergence of the defined processes and study their main statistical properties. The question of genericity (universality) of limit multifractal processes is addressed within this new framework. We finally provide a method for numerical simulations and discuss some specific examples.
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