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QUENCHED CENTRAL LIMIT THEOREM FOR DVORETZKY COVERING

Part of: Stochastic processes Limit theorems

Published online by Cambridge University Press:  28 January 2025

YUANYANG CHANG Open the ORCID record for YUANYANG CHANG [Opens in a new window]  and
PING LIU Open the ORCID record for PING LIU [Opens in a new window]
YUANYANG CHANG
Affiliation:
Department of Mathematics, Wuhan University of Technology, Wuhan 430070, P. R. China e-mail: changyy@whut.edu.cn
PING LIU*
Affiliation:
Department of Mathematics, Wuhan University of Technology, Wuhan 430070, P. R. China
*
e-mail: 1724796906@qq.com
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Abstract

Let $\{\omega _n\}_{n\geq 1}$ be a sequence of independent and identically distributed random variables on a probability space $(\Omega , \mathcal {F}, \mathbb {P})$, each uniformly distributed on the unit circle $\mathbb {T}$, and let $\ell _n=cn^{-\tau }$ for some $c>0$ and $0<\tau <1$. Let $I_{n}=(\omega _n,\omega _n+\ell _n)$ be the random interval with left endpoint $\omega _n$ and length $\ell _n$. We study the asymptotic property of the covering time $N_n(x)=\sharp \{1\leq k\leq n: x\in I_k\}$ for each $x\in \mathbb {T}$. We prove the quenched central limit theorem for the covering time, that is, $\mathbb {P}$-almost surely,

$$ \begin{align*}\frac{N_n(x)-\mathbb{E}_{\mathbb{P}}(N_n(x))}{\sqrt{\sum_{k=1}^n \ell_k(1-\ell_k)}}\end{align*} $$

converges in law to the standard normal distribution.

Keywords

quenched central limit theoremDvoretzky covering

MSC classification

Primary: 60G50: Sums of independent random variables; random walks
Secondary: 60F05: Central limit and other weak theorems
Type
Research Article
Information
Bulletin of the Australian Mathematical Society , First View , pp. 1 - 11
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Australian Mathematical Publishing Association Inc

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Footnotes

This work is supported by NSFC 11901204 and 12271418.

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