arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.14381math.PR

反射随机游走无界可加泛函的样本路径大偏差

Sample-path large deviations for unbounded additive functionals of the reflected random walk

Mihail Bazhba, Jose Blanchet, Chang-Han Rhee, Bert Zwart

AI总结:

本文研究 Lindley 递归 Markov 链无界泛函的样本路径大偏差,利用再生周期分解在 M_1' 拓扑下证明次线性速度 LDP,并揭示 MRW 繁忙期面积的重尾特性。

摘要:

我们证明了由 Lindley 递归诱导的某些 Markov 链的无界泛函具有次线性速度的样本路径大偏差原理(LDP)。该 LDP 在配备 M_1' 拓扑的 Skorokhod 空间 D[0,T] 中成立。我们的技术关键在于依据再生周期对 Markov 链进行适当分解。每个再生周期表示反射随机游走在繁忙期内累积的面积。我们证明了 MRW 繁忙期下方面积的大偏差原理,并表明其呈现重尾行为。

英文摘要:

We prove a sample path large deviation principle (LDP) with sub-linear speed for unbounded functionals of certain Markov chains induced by the Lindley recursion. The LDP holds in the Skorokhod space $\mathbb{D}[0,T]$ equipped with the $M_1'$ topology. Our technique hinges on a suitable decomposition of the Markov chain in terms of regeneration cycles. Each regeneration cycle denotes the area accumulated during the busy period of the reflected random walk. We prove a large deviation principle for the area under the busy period of the MRW, and we show that it exhibits a heavy-tailed behavior.

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。

↑