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Large deviations for neutral functional SDEs with jumps

机译:带跳线的中性功能性SDE的较大偏差

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摘要

In this paper, by the weak convergence method, based on a variational representation for positive functionals of a Poisson random measure and Brownian motion, we establish uniform large deviation principles (LDPs) for a class of neutral stochastic differential equations driven by jump processes. As a byproduct, we also obtain uniform LDPs for neutral stochastic differential delay equations which, in particular, allow the coefficients to be highly nonlinear with respect to the delay argument.
机译:本文采用弱收敛方法,基于泊松随机测度正函数和布朗运动的变分表示,建立了一类由跳跃过程驱动的中立型随机微分方程的统一大偏差原理(LDPs)。作为副产品,我们还为中立型随机微分延迟方程式获得了均匀的LDP,特别是相对于延迟参数,该系数允许高度非线性。

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