First exit and Dirichlet problem for the nonisotropic tempered $$\alpha$$ -stable processes

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Xing Liu, Weihua Deng
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引用次数: 0

Abstract

This paper discusses the first exit and Dirichlet problems of the nonisotropic tempered \(\alpha\)-stable process \(X_t\). The upper bounds of all moments of the first exit position \(\left| X_{\tau _D}\right|\) and the first exit time \(\tau _D\) are explicitly obtained. It is found that the probability density function of \(\left| X_{\tau _D}\right|\) or \(\tau _D\) exponentially decays with the increase of \(\left| X_{\tau _D}\right|\) or \(\tau _D\), and \(\mathrm{E}\left[ \tau _D\right] \sim \mathrm{E}\left[ \left| X_{\tau _D}-\mathrm{E}\left[ X_{\tau _D}\right] \right| ^2\right]\), \(\mathrm{E}\left[ \tau _D\right] \sim \left| \mathrm{E}\left[ X_{\tau _D}\right] \right|\). Next, we obtain the Feynman–Kac representation of the Dirichlet problem by employing the semigroup theory. Furthermore, averaging the generated trajectories of the stochastic process leads to the solution of the Dirichlet problem, which is also verified by numerical experiments.

Abstract Image

非各向同性节制 $$\alpha$$ 稳定过程的首次出口和德里赫特问题
本文讨论了非各向同性的回火(α)-稳定过程 \(X_t\)的第一次出口问题和迪里夏特问题。明确得到了第一次退出位置 \(\left| X_{\tau _D}\right|\) 和第一次退出时间 \(\tau _D\) 的所有矩的上界。结果发现,随着 \(\left| X_{\tau _D}\right|\) 或 \(\tau _D\) 的增加,\(\left| X_{\tau _D}\right|\) 或 \(\tau _D\) 的概率密度函数呈指数衰减、and \(\mathrm{E}\left[ \tau _D\right] \sim \mathrm{E}\left[ \left| X_{tau _D}-\mathrm{E}\left[ X_{tau _D}\right] \right| ^2\right]\)、\(\mathrm{E}\left[ \tau _D\right] \sim \left| \mathrm{E}\left[ X_{tau _D}\right] \right|\)。接下来,我们利用半群理论得到迪里夏特问题的费曼-卡克表示。此外,对随机过程生成的轨迹进行平均,就可以得到迪里夏特问题的解,这也得到了数值实验的验证。
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来源期刊
Computational Statistics
Computational Statistics 数学-统计学与概率论
CiteScore
2.90
自引率
0.00%
发文量
122
审稿时长
>12 weeks
期刊介绍: Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.
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