We consider Gaussian subordinated L\'evy fields (GSLFs) that arise by subordinating L\'evy processes with positive transformations of Gaussian random fields on some spatial domain $\mathcal{D}\subset \mathbb{R}^d$, $d\geq 1$. The resulting random fields are distributionally flexible and have in general discontinuous sample paths. Theoretical investigations of the random fields include pointwise distributions, possible approximations and their covariance function. As an application, a random elliptic PDE is considered, where the constructed random fields occur in the diffusion coefficient. Further, we present various numerical examples to illustrate our theoretical findings
International audienceThis paper presents new results allowing an unknown non-Gaussian positive-defi...
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical ...
The article studies non-Gaussian extensions of a recently discovered link between certain Gaussian r...
General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simp...
AbstractWe consider a Gaussian process P on s(Rd) generated by a polynomial in the Laplace operator....
Approximation of elliptic PDEs with random diffusion coefficients typically requires a representatio...
We study the Gaussian random fields indexed by Rd whose covariance is defined in all generality as t...
International audienceIn this paper we study modulus of continuity and rate of convergence of series...
Graduation date: 2013This dissertation examines properties and representations of several isotropic ...
International audienceWe study pathwise invariances and degeneracies of random fields with motivatin...
Summary. Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial stat...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
Many earth and environmental (as well as a host of other) variables, Y, and their spatial (or tempor...
In this article, we fully characterize the measurable Gaussian processes $(U(x))_{x\in\mathcal{D}}$ ...
Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical m...
International audienceThis paper presents new results allowing an unknown non-Gaussian positive-defi...
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical ...
The article studies non-Gaussian extensions of a recently discovered link between certain Gaussian r...
General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simp...
AbstractWe consider a Gaussian process P on s(Rd) generated by a polynomial in the Laplace operator....
Approximation of elliptic PDEs with random diffusion coefficients typically requires a representatio...
We study the Gaussian random fields indexed by Rd whose covariance is defined in all generality as t...
International audienceIn this paper we study modulus of continuity and rate of convergence of series...
Graduation date: 2013This dissertation examines properties and representations of several isotropic ...
International audienceWe study pathwise invariances and degeneracies of random fields with motivatin...
Summary. Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial stat...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
Many earth and environmental (as well as a host of other) variables, Y, and their spatial (or tempor...
In this article, we fully characterize the measurable Gaussian processes $(U(x))_{x\in\mathcal{D}}$ ...
Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical m...
International audienceThis paper presents new results allowing an unknown non-Gaussian positive-defi...
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical ...
The article studies non-Gaussian extensions of a recently discovered link between certain Gaussian r...