AbstractIn this paper we study the convergence rate of the numerical approximation of the quantiles of the marginal laws of (Xt), where (Xt) is a diffusion process, when one uses a Monte Carlo method combined with the Euler discretization scheme. Our convergence rate estimates are obtained under two sets of hypotheses: either (Xt) is uniformly hypoelliptic (in the sense of condition (UH) below), or the inverse of the Malliavin covariance of the marginal law under consideration satisfies condition (M) below.In order to deduce the required numerical parameters from our error estimates in view of a prescribed accuracy, one needs to get an as accurate as possible lower bound estimate for the density of the marginal law under consideration. This...
27 p.International audienceReflected diffusions in polyhedral domains are commonly used as approxima...
Abstract. We consider the convergence of a continuous-time Markov chain approximation Xh, h> 0, t...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
In this paper we study the convergence rate of the numerical approximation of the quantiles of the m...
29 pagesWe propose a new approach to quantize the marginals of the discrete Euler diffusion proces...
13 pagesInternational audienceIn this work, we approximate a diffusion process by its Euler scheme a...
We study the convergence of Monte Carlo estimators of derivatives when the transition density of the...
For a stochastic differential equation driven by a fractional Brownian motion with Hurst parameter H...
AbstractWe are interested in approximating a multidimensional hypoelliptic diffusion process (Xt)t⩾0...
The aim of this paper is to approximate the expectation of a large class of functionals of the solut...
This paper studies the rate of convergence of an appropriate discretization scheme of the solutio...
In this work, we approximate a diffusion process by its Euler scheme and we study the conver-gence o...
Abstract. Convergence rates of adaptive algorithms for weak approximations of Ito ̂ stochastic diffe...
Dans cet article, nous étudions les distributions limites d'estimateurs de Monte Carlo de processus ...
Assume that one observes the kth,2kth,…,nkth value of a Markov chain X1,h,…,Xnk,h. That means we ass...
27 p.International audienceReflected diffusions in polyhedral domains are commonly used as approxima...
Abstract. We consider the convergence of a continuous-time Markov chain approximation Xh, h> 0, t...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
In this paper we study the convergence rate of the numerical approximation of the quantiles of the m...
29 pagesWe propose a new approach to quantize the marginals of the discrete Euler diffusion proces...
13 pagesInternational audienceIn this work, we approximate a diffusion process by its Euler scheme a...
We study the convergence of Monte Carlo estimators of derivatives when the transition density of the...
For a stochastic differential equation driven by a fractional Brownian motion with Hurst parameter H...
AbstractWe are interested in approximating a multidimensional hypoelliptic diffusion process (Xt)t⩾0...
The aim of this paper is to approximate the expectation of a large class of functionals of the solut...
This paper studies the rate of convergence of an appropriate discretization scheme of the solutio...
In this work, we approximate a diffusion process by its Euler scheme and we study the conver-gence o...
Abstract. Convergence rates of adaptive algorithms for weak approximations of Ito ̂ stochastic diffe...
Dans cet article, nous étudions les distributions limites d'estimateurs de Monte Carlo de processus ...
Assume that one observes the kth,2kth,…,nkth value of a Markov chain X1,h,…,Xnk,h. That means we ass...
27 p.International audienceReflected diffusions in polyhedral domains are commonly used as approxima...
Abstract. We consider the convergence of a continuous-time Markov chain approximation Xh, h> 0, t...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...