Sieve bootstrap test for multiple change points in the mean of long memory sequence

Wenzhi Zhao; Dou Liu; Huiming Wang; Wenzhi Zhao; Dou Liu; Huiming Wang

doi:10.3934/math.2022570

AIMS Mathematics

2022, Volume 7, Issue 6: 10245-10255. doi: 10.3934/math.2022570

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Sieve bootstrap test for multiple change points in the mean of long memory sequence

School of Science, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China

Received: 16 December 2021 Revised: 05 March 2022 Accepted: 14 March 2022 Published: 22 March 2022
MSC : 62F05, 62M10

In this paper, the sieve bootstrap test for multiple change points in the mean of long memory sequence is studied. Firstly, the ANOVA test statistics for change points detection is obtained. Secondly, sieve bootstrap statistics is constructed and the consistency under the Mallows measure is proved. Finally, the effectiveness of the method was illustrated by simulation and example analysis. Simulation results show that our method can not only control the empirical size well but also have reasonable good power.
- long memory,
- multiple change points,
- ANOVA,
- sieve bootstrap,
- test
Citation: Wenzhi Zhao, Dou Liu, Huiming Wang. Sieve bootstrap test for multiple change points in the mean of long memory sequence[J]. AIMS Mathematics, 2022, 7(6): 10245-10255. doi: 10.3934/math.2022570

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Abstract

In this paper, the sieve bootstrap test for multiple change points in the mean of long memory sequence is studied. Firstly, the ANOVA test statistics for change points detection is obtained. Secondly, sieve bootstrap statistics is constructed and the consistency under the Mallows measure is proved. Finally, the effectiveness of the method was illustrated by simulation and example analysis. Simulation results show that our method can not only control the empirical size well but also have reasonable good power.

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