An analogue of Law of Iterated Logarithm for Heavy Tailed Random Variables

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Hauptverfasser: Dolgopyat, Dmitry, Liu, Sixu
Format: Preprint
Veröffentlicht: 2023
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author Dolgopyat, Dmitry
Liu, Sixu
author_facet Dolgopyat, Dmitry
Liu, Sixu
contents We establish functional limit theorems for ergodic sums of observables with power singularities for expanding circle maps. In the regime where the observables have infinite variance, we show that when rescaled by $N^{1/s}(\ln N)^α$, the partial sum process has limit points consisting precisely of increasing piecewise constant functions with finitely many jumps. Our approach combines trimming techniques with a multiple Borel-Cantelli argument. It provides a functional law of the iterated logarithm for heavy-tailed processes where classical almost sure invariance principles do not apply.
format Preprint
id arxiv_https___arxiv_org_abs_2312_15378
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle An analogue of Law of Iterated Logarithm for Heavy Tailed Random Variables
Dolgopyat, Dmitry
Liu, Sixu
Dynamical Systems
Probability
We establish functional limit theorems for ergodic sums of observables with power singularities for expanding circle maps. In the regime where the observables have infinite variance, we show that when rescaled by $N^{1/s}(\ln N)^α$, the partial sum process has limit points consisting precisely of increasing piecewise constant functions with finitely many jumps. Our approach combines trimming techniques with a multiple Borel-Cantelli argument. It provides a functional law of the iterated logarithm for heavy-tailed processes where classical almost sure invariance principles do not apply.
title An analogue of Law of Iterated Logarithm for Heavy Tailed Random Variables
topic Dynamical Systems
Probability
url https://arxiv.org/abs/2312.15378