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Approximation of stochastic differential equations driven by subfractional Brownian motion at discrete time observation
Communications in Statistics - Theory and Methods ( IF 0.8 ) Pub Date : 2021-03-23 , DOI: 10.1080/03610926.2021.1901924 Guangjun Shen 1, 2 , Zheng Tang 1 , Jun Wang 3
中文翻译:
离散时间观测下次分数布朗运动驱动的随机微分方程的逼近
更新日期:2021-03-23
Communications in Statistics - Theory and Methods ( IF 0.8 ) Pub Date : 2021-03-23 , DOI: 10.1080/03610926.2021.1901924 Guangjun Shen 1, 2 , Zheng Tang 1 , Jun Wang 3
Affiliation
Abstract
In this paper, we consider discrete time approximations for stochastic differential equations with the form: where is a continuous function with locally bounded variation, are measurable functions, and the integral with respect to is the pathwise Riemann-Stieltjes integral, SH is a subfractional Brownian motion with σ is a deterministic (possibly discontinuous) function.
中文翻译:
离散时间观测下次分数布朗运动驱动的随机微分方程的逼近
摘要
在本文中,我们考虑具有以下形式的随机微分方程的离散时间近似:在哪里是具有局部有界变化的连续函数,是可测函数,积分是关于是路径 Riemann-Stieltjes 积分,S H是次分数布朗运动,其中 σ是确定性(可能不连续)函数。