土耳其COVID-19数据的Itô随机微分方程拟合

Sevda Özdemir Çalikuşu, F. Erdoğan
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引用次数: 0

摘要

在本研究中,采用随机微分方程模型(SDEM)调查了土耳其的COVID-19数据。首先,利用极大似然法估计了上述流行病中发生的SDE的参数。然后,根据给定的COVID-19数据,得到合理的随机微分方程(SDE)。此外,应用欧拉-丸山近似方法得到了SDE的运动轨迹。轨迹的性能由卡方标准确定。采用统计软件R-Studio进行统计。这些结果也得到了图形表示的证实。《玛纳斯工程杂志》版权归吉尔吉斯-土耳其玛纳斯大学所有,未经版权所有者明确书面许可,其内容不得复制或通过电子邮件发送到多个网站或发布到listserv。但是,用户可以打印、下载或通过电子邮件发送文章供个人使用。这可以删节。对副本的准确性不作任何保证。用户应参阅原始出版版本的材料的完整。(版权适用于所有人。)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fitting The Itô Stochastic Differential Equation to The COVID-19 Data in Turkey
In this study, COVID-19 data in Turkey is investigated by Stochastic Differential Equation Modeling (SDEM). Firstly, parameters of SDE which occur in mentioned epidemic problem are estimated by using the maximum likelihood procedure. Then, we have obtained reasonable Stochastic Differential Equation (SDE) based on the given COVID-19 data. Moreover, by applying Euler-Maruyama Approximation Method trajectories of SDE are achieved. The performances of trajectories are established by Chi-Square criteria. The results are acquired by using statistical software R-Studio. These results are also corroborated by graphical representation. [ FROM AUTHOR] Copyright of Manas Journal of Engineering is the property of Kyrgyz-Turkish Manas University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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