activity
20102023
most citedCoupling property and gradient estimates of Lévy processes via the symbol

55 citations · 289 across the 73 of their papers we have counts for

collaborators

73 papers

math.PR2023

Quasi-stationary distribution for continuous-state branching processes with competition

Pei-Sen Li, Jian Wang, Xiaowen Zhou

We study quasi-stationary distribution of the continuous-state branching process with competition introduced in Berestycki, Fittipaldi and Fontbona\ (Probab. Theory Relat. Fields,…

math.PR2022

Limit theorems for some long range random walks on torsion free nilpotent groups

Zhen-Qing Chen, Takashi Kumagai, Laurent Saloff-Coste +2

We consider a natural class of long range random walks on torsion free nilpotent groups and develop limit theorems for these walks. Given the original discrete group and a rand…

math.PR2022

Exponential ergodicity for damping Hamiltonian dynamics with state-dependent and non-local collisions

Jianhai Bao, Jian Wang

In this paper, we investigate the exponential ergodicity in a Wasserstein-type distance for a damping Hamiltonian dynamics with state-dependent and non-local collisions, which inde…

math.PR2021

Strict Kantorovich contractions for Markov chains and Euler schemes with general noise

Lu-Jing Huang, Mateusz B. Majka, Jian Wang

We study contractions of Markov chains on general metric spaces with respect to some carefully designed distance-like functions, which are comparable to the total variation and the…

math.PR2021

Heat kernels for reflected diffusions with jumps on inner uniform domains

Zhen-Qing Chen, Panki Kim, Takashi Kumagai +1

In this paper, we study sharp two-sided heat kernel estimates for a large class of symmetric reflected diffusions with jumps on the closure of an inner uniform domain in a leng…

math.PR2021

Coupling approach for exponential ergodicity of stochastic Hamiltonian systems with Lévy noises

Jianhai Bao, Jian Wang

We establish exponential ergodicity for the stochastic Hamiltonian system on with Lévy noises \begin{align*} \begin{cases} \mathrm{d} X_t=\bi…