Abstract
We study the robust quickest change detection under unknown pre- and post-change distributions. To deal with uncertainties in the data-generating distributions, we formulate two data-driven ambiguity sets based on the Wasserstein distance, without any parametric assumptions. The minimax robust test is constructed as the CUSUM test under least favorable distributions, a representative pair of distributions in the ambiguity sets. We show that the minimax robust test can be obtained in a tractable way and is asymptotically optimal. We investigate the effectiveness of the proposed robust test over existing methods, including the generalized likelihood ratio test and the robust test under KL divergence based ambiguity sets.
| Original language | English (US) |
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| Title of host publication | 2022 IEEE International Symposium on Information Theory, ISIT 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1909-1914 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665421591 |
| DOIs | |
| State | Published - 2022 |
| Externally published | Yes |
| Event | 2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland Duration: Jun 26 2022 → Jul 1 2022 |
Publication series
| Name | IEEE International Symposium on Information Theory - Proceedings |
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| Volume | 2022-June |
| ISSN (Print) | 2157-8095 |
Conference
| Conference | 2022 IEEE International Symposium on Information Theory, ISIT 2022 |
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| Country/Territory | Finland |
| City | Espoo |
| Period | 6/26/22 → 7/1/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- CUSUM test
- Least favorable distributions
- Robust change detection
- Wasserstein metric