Robust Quickest Change Detection in Non-Stationary Processes
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866908360953036800 |
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| author | Hou, Yingze Oleyaeimotlagh, Yousef Mishra, Rahul Bidkhori, Hoda Banerjee, Taposh |
| author_facet | Hou, Yingze Oleyaeimotlagh, Yousef Mishra, Rahul Bidkhori, Hoda Banerjee, Taposh |
| contents | Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09673 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust Quickest Change Detection in Non-Stationary Processes Hou, Yingze Oleyaeimotlagh, Yousef Mishra, Rahul Bidkhori, Hoda Banerjee, Taposh Methodology Signal Processing Statistics Theory Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness. |
| title | Robust Quickest Change Detection in Non-Stationary Processes |
| topic | Methodology Signal Processing Statistics Theory |
| url | https://arxiv.org/abs/2310.09673 |