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
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home AIMS Mathematics Article
PDF (2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Strong consistency properties of the variance change point estimator based on strong-mixing samples

Mengmei Xi1, Yi Wu2, Xuejun Wang1( )
School of Big Data and Statistics, Anhui University, Hefei, 230026, China
School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, 247000, China
Show Author Information

Abstract

In this paper, our primary attention was centered on the issue of detecting the variance change point for strong-mixing samples. We delved into the cumulative sum (CUSUM) estimator of variance change model and established the strong convergence rate of the variance change point estimation. Furthermore, to corroborate the effectiveness of the CUSUM based methodology, we have conducted a series of simulations, the outcomes of which underscored its validity.

Keywords

variance change pointCUSUMstrong-mixing sequencestrong consistency
CLC number: 62F12

References

【1】
【1】
 
 
Crossref Google Scholar
AIMS Mathematics
Volume 9 Issue 11,
November 2024
Pages 30059-30072
DOI: 10.3934/math.20241452

{{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

Cite this Report
Close
Close
Cite this article:
Xi M, Wu Y, Wang X. Strong consistency properties of the variance change point estimator based on strong-mixing samples. AIMS Mathematics, 2024, 9(11): 30059-30072. https://doi.org/10.3934/math.20241452

1549

Views

52

Downloads

0

Crossref

0

Web of Science

0

Scopus
Received: 25 August 2024
Revised: 27 September 2024
Accepted: 08 October 2024
Published: 23 October 2024
©2024 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)