In this dissertation we consider several limiting criteria forn-dimensional diffusion processes defined as solutions of stochasticdifferential equations. Our main interest is in criteria for polynomialand exponential rates of convergence to the steady state distributionin the total variation norm. Resulting criteria should place assumptionsonly on the coefficients of the elliptic differentialoperator governing the diffusion.Coupling of Harris chains is one of the main methods employed in thisdissertation
We consider problems of optimal stopping where the driving process is a (one- or multi-dimensional) ...
We provide a rate for the strong convergence of Euler approximations for stochastic differential equ...
AbstractWe provide a rate for the strong convergence of Euler approximations for stochastic differen...
Using a coupling technique, rates of convergence in total variation of certain k-dimensional diffusi...
This paper studies diffusion processes constrained to the positive orthant under infinitesimal chang...
The goal of this paper is to present a series of recent contributions arising in numerical probabili...
We study the Euler approximation scheme for solutions of stochastic differential equations with boun...
This work uses techniques from convex analysis to study constrained solutions (u, ƞ) to stochastic ...
journal-articleWe consider diffusion processes killed at the boundary of Riemannian manifolds. The a...
Röckner M, Xie L. Diffusion approximation for fully coupled stochastic differential equations. Annal...
We give recurrence/transience criteria for skew products of one dimensional diffusion process and th...
AbstractThe paper treats approximations to stochastic differential equations with both a diffusion a...
AbstractLet D be either a convex domain in Rd or a domain satisfying the conditions (A) and (B) cons...
Abstract: Stochastic differential equations provide a useful means of intro-ducing stochasticity int...
In this paper we prove a version of the maximum principle, in the sense of Pontryagin, for the optim...
We consider problems of optimal stopping where the driving process is a (one- or multi-dimensional) ...
We provide a rate for the strong convergence of Euler approximations for stochastic differential equ...
AbstractWe provide a rate for the strong convergence of Euler approximations for stochastic differen...
Using a coupling technique, rates of convergence in total variation of certain k-dimensional diffusi...
This paper studies diffusion processes constrained to the positive orthant under infinitesimal chang...
The goal of this paper is to present a series of recent contributions arising in numerical probabili...
We study the Euler approximation scheme for solutions of stochastic differential equations with boun...
This work uses techniques from convex analysis to study constrained solutions (u, ƞ) to stochastic ...
journal-articleWe consider diffusion processes killed at the boundary of Riemannian manifolds. The a...
Röckner M, Xie L. Diffusion approximation for fully coupled stochastic differential equations. Annal...
We give recurrence/transience criteria for skew products of one dimensional diffusion process and th...
AbstractThe paper treats approximations to stochastic differential equations with both a diffusion a...
AbstractLet D be either a convex domain in Rd or a domain satisfying the conditions (A) and (B) cons...
Abstract: Stochastic differential equations provide a useful means of intro-ducing stochasticity int...
In this paper we prove a version of the maximum principle, in the sense of Pontryagin, for the optim...
We consider problems of optimal stopping where the driving process is a (one- or multi-dimensional) ...
We provide a rate for the strong convergence of Euler approximations for stochastic differential equ...
AbstractWe provide a rate for the strong convergence of Euler approximations for stochastic differen...