ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916556380831744 |
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| author | Cui, Xiaolong Geng, Haoyu Wang, Guanghui Wang, Zhaojun Zou, Changliang |
| author_facet | Cui, Xiaolong Geng, Haoyu Wang, Guanghui Wang, Zhaojun Zou, Changliang |
| contents | We introduce ART, a distribution-free and model-agnostic framework for changepoint detection that provides finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function, ensuring exchangeability in the absence of changepoints. These scores are then ranked and aggregated to detect distributional changes. The resulting test offers exact Type-I error control, agnostic to specific distributional or model assumptions. Moreover, ART seamlessly extends to multi-scale settings, enabling robust multiple changepoint estimation and post-detection inference with finite-sample error rate control. By locally ranking the scores and performing aggregations across multiple prespecified intervals, ART identifies changepoint intervals and refines subsequent inference while maintaining its distribution-free and model-agnostic nature. This adaptability makes ART as a reliable and versatile tool for modern changepoint analysis, particularly in high-dimensional data contexts and applications leveraging machine learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees Cui, Xiaolong Geng, Haoyu Wang, Guanghui Wang, Zhaojun Zou, Changliang Methodology Statistics Theory We introduce ART, a distribution-free and model-agnostic framework for changepoint detection that provides finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function, ensuring exchangeability in the absence of changepoints. These scores are then ranked and aggregated to detect distributional changes. The resulting test offers exact Type-I error control, agnostic to specific distributional or model assumptions. Moreover, ART seamlessly extends to multi-scale settings, enabling robust multiple changepoint estimation and post-detection inference with finite-sample error rate control. By locally ranking the scores and performing aggregations across multiple prespecified intervals, ART identifies changepoint intervals and refines subsequent inference while maintaining its distribution-free and model-agnostic nature. This adaptability makes ART as a reliable and versatile tool for modern changepoint analysis, particularly in high-dimensional data contexts and applications leveraging machine learning methods. |
| title | ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2501.04475 |