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- W4387582603 abstract "This paper considers applying the block wild bootstrap (BWB) approach to self-normalization method in detecting mean changes on the time series. The performance of the BWB approach is compared with three existing methods: CUSUM with asymptotic p–value, SN with asymptotic p-value, and CUSUM with BWB. Furthermore, we examine the robustness of the BWB method by considering several block lengths. Our results show that the BWB alleviate size distortions observed in the SN method, in particular with small sample sizes and strong correlations, while maintaining reasonable powers. Also, contrary to CUSUM method, the block length for SN method is robust so that it almost free block length selection." @default.
- W4387582603 created "2023-10-13" @default.
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- W4387582603 date "2023-09-30" @default.
- W4387582603 modified "2023-10-14" @default.
- W4387582603 title "Block wild bootstrap for self-normalization based change-point detection" @default.
- W4387582603 doi "https://doi.org/10.7465/jkdi.2023.34.5.823" @default.
- W4387582603 hasPublicationYear "2023" @default.
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