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Packing dimension results for anisotropic Gaussian random fields

Authors :
Dongsheng Wu
Yimin Xiao
Anne Estrade
Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145)
Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)
Department of Mathematical Sciences
University of Alabama [Tuscaloosa] (UA)
Department of Statistics and Probability (Michigan State University)
Michigan State University [East Lansing]
Michigan State University System-Michigan State University System
ANR-09-BLAN-0029,MATAIM,Modélisation de l'Anisotropie de Textures. Applications à l'Imagerie Médicale(2009)
Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 )
Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS )
University of Alabama [Tuscaloosa] ( UA )
MATAIM, ANR-09-BLAN-029,MATAIM, ANR-09-BLAN-029
ANR-09-BLAN-0029,MATAIM,Modélisation de l'Anisotropie de Textures. Applications à l'Imagerie Médicale.(2009)
Estrade, Anne
Blanc - Modélisation de l'Anisotropie de Textures. Applications à l'Imagerie Médicale. - - MATAIM2009 - ANR-09-BLAN-0029 - Blanc - VALID
Source :
communications in stochastic analysis, communications in stochastic analysis, 2011, 5 (1), pp.41-64
Publication Year :
2011
Publisher :
Louisiana State University Libraries, 2011.

Abstract

International audience; Let $X=\{X(t), t \in \R^N\}$ be a Gaussian random field with values in $\R^d$ defined by $$X(t) = \big(X_1(t), \ldots, X_d(t)\big), \qquad \forall \ t \in \R^N, $$ where $X_1, \ldots, X_d$ are independent copies of a centered real-valued Gaussian random field $X_0$. We consider the case when $X_0$ is anisotropic and study the packing dimension of the range $X(E)$, where $E\subseteq \R^N$ is a Borel set. For this purpose we extend the original notion of packing dimension profile due to Falconer and Howroyd (1997) to the anisotropic metric space $(\R^N, \rho)$, where $\rho(s, t) = \sum_{j=1}^N |s_j - t_j|^{H_j}$ and $(H_1, \ldots, H_N) \in (0, 1)^N$ is a given vector. The extended notion of packing dimension profile is of independent interest.

Details

ISSN :
09739599
Volume :
5
Database :
OpenAIRE
Journal :
Communications on Stochastic Analysis
Accession number :
edsair.doi.dedup.....5baccebff8c69ab8dad0f5803641b4c3