Non-parametric multiple change-point detection
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908343851810816 |
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| author | Anastasiou, Andreas Fryzlewicz, Piotr |
| author_facet | Anastasiou, Andreas Fryzlewicz, Piotr |
| contents | We introduce a methodology, labelled Non-Parametric Isolate-Detect (NPID), for the consistent estimation of the number and locations of multiple change-points in a non-parametric setting. The method can handle general distributional changes and is based on an isolation technique preventing the consideration of intervals that contain more than one change-point, which enhances the estimation accuracy. As stopping rules, we propose both thresholding and the optimization of an information criterion. In the scenarios tested, which cover a broad range of change types, NPID outperforms the state of the art. An R implementation is provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-parametric multiple change-point detection Anastasiou, Andreas Fryzlewicz, Piotr Statistics Theory We introduce a methodology, labelled Non-Parametric Isolate-Detect (NPID), for the consistent estimation of the number and locations of multiple change-points in a non-parametric setting. The method can handle general distributional changes and is based on an isolation technique preventing the consideration of intervals that contain more than one change-point, which enhances the estimation accuracy. As stopping rules, we propose both thresholding and the optimization of an information criterion. In the scenarios tested, which cover a broad range of change types, NPID outperforms the state of the art. An R implementation is provided. |
| title | Non-parametric multiple change-point detection |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2504.21379 |