G-中性随机微分方程关于参数的可微分性

IF 0.3 Q4 STATISTICS & PROBABILITY Random Operators and Stochastic Equations Pub Date : 2024-02-20 DOI:10.1515/rose-2024-2005

Zakaria Boumezbeur, H. Boutabia, A. Redjil, O. Kebiri

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引用次数: 0

摘要

本文研究了由 G 布朗运动驱动的中性随机微分方程解关于参数的可微性。在适当的假设条件下，我们证明了解是可微的，且与初始数据中出现的参数有关。此外，还给出了导数的随机微分方程，并证明了解的唯一性。此外，我们还给出了一个例子来说明理论上得到的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Differentiability of G-neutral stochastic differential equations with respect to parameter

In this paper, we study the differentiability of solutions of neutral stochastic differential equations driven by G-Brownian motion with respect to parameter. Under suitable assumptions, we show that solutions are differentiable with respect to the parameter which appears in the initial data. In addition, the stochastic differential equation of the derivative is given and the existence-uniqueness of solution is proved. Moreover, an example to illustrate the theoretically obtained results is presented.

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来源期刊

Random Operators and Stochastic Equations STATISTICS & PROBABILITY-

CiteScore

0.60

自引率

25.00%

发文量