| Autor: |
Hao Chen, Abhishek Gupta, Yin Sun, Ness Shroff |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Open Journal of Control Systems, Vol 3, Pp 202-213 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2694-085X |
| DOI: |
10.1109/OJCSYS.2024.3398530 |
| Popis: |
This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length ($ {\mathtt {ARL}}$) and upper bounds for average-detection-delay ($ {\mathtt {ADD}}$) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast $\phi$-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially $\alpha$/$\beta$-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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