Research Article
Application of Self-normalized Method in Long-memory Multi-Means Change-Point Test
@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
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.
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