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From almost sure local regularity to almost sure Hausdorff dimension for Gaussian fields

Published online by Cambridge University Press:  08 October 2014

Erick Herbin ,
Benjamin Arras  and
Geoffroy Barruel
Erick Herbin
Affiliation:
Ecole Centrale Paris, Grande Voie des Vignes, 92295 Chatenay-Malabry, France. erick.herbin@ecp.fr; benjamin.arras@ecp.fr; geoffroy.barruel@student.ecp.fr
Benjamin Arras
Affiliation:
Ecole Centrale Paris, Grande Voie des Vignes, 92295 Chatenay-Malabry, France. erick.herbin@ecp.fr; benjamin.arras@ecp.fr; geoffroy.barruel@student.ecp.fr
Geoffroy Barruel
Affiliation:
Ecole Centrale Paris, Grande Voie des Vignes, 92295 Chatenay-Malabry, France. erick.herbin@ecp.fr; benjamin.arras@ecp.fr; geoffroy.barruel@student.ecp.fr
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Abstract

Fine regularity of stochastic processes is usually measured in a local way by local Hölder exponents and in a global way by fractal dimensions. In the case of multiparameter Gaussian random fields, Adler proved that these two concepts are connected under the assumption of increment stationarity property. The aim of this paper is to consider the case of Gaussian fields without any stationarity condition. More precisely, we prove that almost surely the Hausdorff dimensions of the range and the graph in any ball B(t0,ρ) are bounded from above using the local Hölder exponent at t0. We define the deterministic local sub-exponent of Gaussian processes, which allows to obtain an almost sure lower bound for these dimensions. Moreover, the Hausdorff dimensions of the sample path on an open interval are controlled almost surely by the minimum of the local exponents. Then, we apply these generic results to the cases of the set-indexed fractional Brownian motion on RN, the multifractional Brownian motion whose regularity function H is irregular and the generalized Weierstrass function, whose Hausdorff dimensions were unknown so far.

Keywords

Gaussian processesHausdorff dimension(multi)fractional Brownian motionmultiparameter processesHölder regularitystationarity
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 18 , 2014 , pp. 418 - 440
Copyright
© EDP Sciences, SMAI 2014

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