Application of Self-normalized Method in Long-memory Multi-Means Change-Point Test

Liu Yi

Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China

Research Article

Application of Self-normalized Method in Long-memory Multi-Means Change-Point Test

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.26-5-2023.2334401,
    author={Liu  Yi},
    title={Application of Self-normalized Method in Long-memory Multi-Means Change-Point Test},
    proceedings={Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26--28, 2023, Nanjing, China},
    publisher={EAI},
    proceedings_a={MSEA},
    year={2023},
    month={7},
    keywords={long memory time series change points self-normalized},
    doi={10.4108/eai.26-5-2023.2334401}
}

Liu Yi
Year: 2023
Application of Self-normalized Method in Long-memory Multi-Means Change-Point Test
MSEA
EAI
DOI: 10.4108/eai.26-5-2023.2334401

Liu Yi¹^,*

1: Xi'an University of Science and Technology

*Contact email: Wuoshiwzm@hotmail.com

Abstract

In this paper, we have developed a novel attempt to be sensitive to multiple means of long-memory time series in an unsupervised manner using our own legitimate method. Self-regular, can avoid estimating the gradual variance variance and use the regular method at the same time. The method can be conveniently and conveniently applied to the first-order stationary data with long memory (stationary with long memory) dependency), no change points, statistics are collected and summarized in non-exit distribution. We describe this statistic and evaluate its effects. At the same time, the feasibility of the method is illustrated by real data.

Keywords: long memory time series change points self-normalized

Published: 2023-07-21
Publisher: EAI

: http://dx.doi.org/10.4108/eai.26-5-2023.2334401