activity
20082023
most citedTwo-time-scale stochastic partial differential equations driven by -stable noises: Averaging principles

80 citations · 136 across the 69 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

math.PR2019★ 1 cited

CLT and MDP for McKean-Vlasov SDEs

Yongqiang Suo, Chenggui Yuan

Under a Lipschitz condition on distribution dependent coefficients, the central limit theorem and the moderate deviation principle are obtained for solutions of McKean-Vlasov type…

math.PR2019★ 2 cited

Weak convergence of path-dependent SDEs driven by fractional Brownian motion with irregular coefficients

Yongqiang Suo, Chenggui Yuan, shaoqin Zhang

In this paper, by using Girsanov's transformation and the property of the corresponding reference stochastic differential equations, we investigate weak existence and uniqueness of…

math.NA2019

Multilevel Monte Carlo theta EM scheme for SDDEs with small noise

Li Tan, Chenggui Yuan

In this paper, a multilevel Monte Carlo theta EM scheme is provided for stochastic differential delay equations with small noise. Under a global Lipschitz condition, the variance o…

math.PR2019

Comparison Theorem for Distribution Dependent Neutral SFDEs

Xing Huang, Chenggui Yuan

In this paper, the existence and uniqueness of strong solutions to distribution dependent neutral SFDEs are proved. We give the conditions such that the order preservation of these…

math.PR2019

Limit Theorems for Additive Functionals of Path-Dependent SDEs

Jianhai Bao, Feng-Yu Wang, Chenggui Yuan

By using limit theorems of uniform mixing Markov processes and martingale difference sequences, the strong law of large numbers, central limit theorem, and the law of iterated loga…

math.PR2019

Large deviations for neutral stochastic functional differential equations

Yongqiang Suo, Chenggui Yuan

In this paper, under a one-sided Lipschitz condition on the drift coefficient we adopt (via contraction principle) a exponential approximation argument to investigate large deviati…