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arXiv 2103.12093math.PR
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布朗过滤抽样的收缩准则

Contraction criteria for Brownian filtrations samplings

Rémi Lassalle

摘要:

本文研究了一个给定的随机过程是否产生抽样布朗过滤的问题。利用Frank H. Clarke收缩准则,其构造依赖于采样Wiener测度的准不变性。后者是从这个采样的布朗运动背后的精确的解析结构。特别地,Cameron-Martin空间从通常的Guseinov测度上的勒贝格积分中识别出来。作为应用,我们得到了一类由采样布朗运动驱动的非马氏随机微分方程解存在的充分条件.通过取与单位时间间隔一致的采样时间集,结果适用于连续时间模型。

英文摘要:

This paper investigates the problem to determine whether a given stochastic process generates a sampled Brownian filtration. A fairly general sufficient condition is obtained by applying the Frank H. Clarke contraction criteria to a functional whose construction relies on the quasi-invariance properties of a sampled Wiener measure. The latter are investigated from the precise analytic structure underlying this sampled Brownian motion. In particular, the Cameron-Martin space is identified from usual Lebesgue integrals over a Guseinov measure. As an application we obtain sufficient conditions for the existence of a solution to a class of not necessarily Markovian stochastic differential equations driven by a sampled Brownian motion. By taking a sampling times set which coincides with the unit interval, the results apply to continuous time models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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