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arXiv 2601.04863math.PR
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随机矩阵乘积收敛于稳定分布

Convergence to Stable Laws for Products of Random Matrices

Université de Tours, Université d’Orléans, CNRS, IDP(图尔大学,奥尔良大学,法国国家科学研究中心,IDP)

Axel Péneau

AI总结:

本文研究了随机矩阵乘积的log-norm满足广义中心极限定理,并通过构造关键时间点证明了矩增益,同时探讨了高阶扩展的可能。

摘要:

在合理的代数假设和无限二阶矩假设下,我们证明了随机独立同分布矩阵乘积的范数(log-norm)满足广义中心极限定理(GCLT),即Paul Lévi的意义。证明基于差值Δ_n的弱大数定律,该差值表示前n个矩阵乘积的log-norm与它们log-norm之和的差。这个弱大数定律意味着Δ_n的行为类似于具有有限二阶矩的i.i.d.随机变量之和,只要每个矩阵的log-norm具有有限的q阶矩(q>0)。这个矩的增益是本文的核心结果,基于构造了关键时间点。此外,这些结果在考虑完整的Cartan投影而不是log-norm时具有良好的高阶扩展。

英文摘要:

Under reasonable algebraic assumptions and under an infinite second order moment assumption, we show that the logarithm of the norm (log-norm) of a product of random i.i.d. matrices with entries in $\mathbb{R}$ or in any other local field satisfies a generalized Central Limit Theorem (GCLT) in the sense of Paul Lévi. The proof is based on a weak law of large number for the difference $Δ_n$ between the log-norm of the product of the first $n$ matrices and the sum of their log-norms. This weak law of large numbers morally says that $Δ_n$ behaves like a sum of i.i.d. random variables that have a finite moment of order $2q$ as long as the log-norm of each matrices has a finite moment of order $q$ for a given $q > 0$. This gain of moment is the central result of the present paper and is based on the construction of pivotal times. Moreover, these results admit a nice higher rank extension when one looks at the full Cartan projection instead of the log-norm.

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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