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arXiv 2112.09321math.PR
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关于最小随机游走的新见解

New insights on the minimal random walk

Bernard Bercu, Víctor Hugo Vázquez Guevara

AI总结:

本文采用新的鞅方法,深入分析了最小随机游走(MRW)在扩散、临界和超扩散三种状态下的渐近行为,建立了几乎必然收敛、二次强定律、重对数律及波动高斯性等新结果。

摘要:

本文旨在通过一种新的鞅方法,深化对所谓最小随机游走(MRW)渐近行为的分析。MRW是一种具有无限记忆的离散时间随机游走,根据其两个参数的位置,存在三种状态。在扩散态和临界态中,我们建立了MRW几乎必然渐近行为的新结果,如二次强定律和重对数律。在超扩散态中,我们证明了适当归一化的MRW几乎必然收敛于一个非退化的随机变量。此外,我们证明了MRW围绕其极限随机变量的波动仍然是高斯的。

英文摘要:

The aim of this paper is to deepen the analysis of the asymptotic behavior of the so-called minimal random walk (MRW) using a new martingale approach. The MRW is a discrete-time random walk with infinite memory that has three regimes depending on the location of its two parameters. In the diffusive and critical regimes, we establish new results on the almost sure asymptotic behavior of the MRW, such as the quadratic strong law and the law of the iterated logarithm. In the superdiffusive regime, we prove the almost sure convergence of the MRW, properly normalized, to a nondegenerate random variable. Moreover, we show that the fluctuation of the MRW around its limiting random variable is still Gaussian.

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。

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