We consider two independent Gaussian processes that admit a representation in terms of a stochastic integral of a deterministic kernel with respect to a standard Wiener process. In this paper we construct two families of processes, from a unique Poisson process, the finite dimensional distributions of which converge in law towards the finite dimensional distributions of the two independent Gaussian processes. As an application of this result we obtain families of processes that converge in law towards fractional Brownian motion and sub-fractional Brownian motion. 1. Introduction an
AbstractUsing the white noise space framework, we construct and study a class of Gaussian processes ...
Author's pre-print draft dated September 2008. Final version published by Cambridge University Press...
We define a new type of self-similarity for one-parameter families of stochastic processes, which ap...
Stochastic Integrals Driven by Isonormal Gaussian Processes and Applications Master Thesis - Petr Čo...
This paper considers the asymptotic distribution of the covariance of a nonstationary frac-tionally ...
AbstractWe study the convergence to the multiple Wiener–Itô integral from processes with absolutely ...
This paper considers the asymptotic distribution of the sample covariance of a nonstationary fractio...
International audience{Let B=(B1(t),...,Bd(t)) be a d-dimensional fractional Brownian motion with Hu...
International audience{Let B=(B1(t),...,Bd(t)) be a d-dimensional fractional Brownian motion with Hu...
We discuss discrete stochastic processes with two independent variables: one is the standard symmetr...
We study a non-Gaussian and non-stable process arising as the limit of sums of rescaled renewal proc...
We consider a family of stationary Gaussian processes that includes the stationary Ornstein-Uhlenbec...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
AbstractUsing the white noise space framework, we construct and study a class of Gaussian processes ...
Author's pre-print draft dated September 2008. Final version published by Cambridge University Press...
We define a new type of self-similarity for one-parameter families of stochastic processes, which ap...
Stochastic Integrals Driven by Isonormal Gaussian Processes and Applications Master Thesis - Petr Čo...
This paper considers the asymptotic distribution of the covariance of a nonstationary frac-tionally ...
AbstractWe study the convergence to the multiple Wiener–Itô integral from processes with absolutely ...
This paper considers the asymptotic distribution of the sample covariance of a nonstationary fractio...
International audience{Let B=(B1(t),...,Bd(t)) be a d-dimensional fractional Brownian motion with Hu...
International audience{Let B=(B1(t),...,Bd(t)) be a d-dimensional fractional Brownian motion with Hu...
We discuss discrete stochastic processes with two independent variables: one is the standard symmetr...
We study a non-Gaussian and non-stable process arising as the limit of sums of rescaled renewal proc...
We consider a family of stationary Gaussian processes that includes the stationary Ornstein-Uhlenbec...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
We investigate the main statistical parameters of the integral over time of the fractional Brownian ...
AbstractUsing the white noise space framework, we construct and study a class of Gaussian processes ...
Author's pre-print draft dated September 2008. Final version published by Cambridge University Press...
We define a new type of self-similarity for one-parameter families of stochastic processes, which ap...