AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (246.5 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Moderate deviation principle for m-dependent random variables under the sub-linear expectation

Shuang GuoYong Zhang( )
School of mathematics, Jilin University, Changchun 130012, China
Show Author Information

Abstract

Let { X n , n 1 } be a sequence of m-dependent strictly stationary random variables in a sub-linear expectation ( Ω , H , E ). In this article, we give the definition of m-dependent sequence of random variables under sub-linear expectation spaces taking values in R . Then we establish moderate deviation principle for this kind of sequence which is strictly stationary. The results in this paper generalize the result that in the case of independent identically distributed samples. It provides a basis to discuss the moderate deviation principle for other types of dependent sequences.

CLC number: 60F10

References

【1】
【1】
 
 
AIMS Mathematics
Pages 5943-5956

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Guo S, Zhang Y. Moderate deviation principle for m-dependent random variables under the sub-linear expectation. AIMS Mathematics, 2022, 7(4): 5943-5956. https://doi.org/10.3934/math.2022331

1

Views

0

Downloads

0

Crossref

0

Web of Science

0

Scopus

Received: 10 November 2021
Revised: 29 December 2021
Accepted: 06 January 2022
Published: 15 April 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)