TUNE: Algorithm-Agnostic Inference after Changepoint Detection
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917784425857024 |
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| author | Jia, Yinxu Liu, Jixuan Wang, Guanghui Wang, Zhaojun Zou, Changliang |
| author_facet | Jia, Yinxu Liu, Jixuan Wang, Guanghui Wang, Zhaojun Zou, Changliang |
| contents | In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attention. Despite advancements through selective p-values, current methodologies often rely on stringent assumptions tied to specific changepoint models and detection algorithms, potentially compromising the accuracy of post-detection statistical inference. We introduce TUNE (Thresholding Universally and Nullifying change Effect), a novel algorithm-agnostic approach that uniformly controls error probabilities across detected changepoints. TUNE sets a universal threshold for multiple test statistics, applicable across a wide range of algorithms, and directly controls the family-wise error rate without the need for selective p-values. Through extensive theoretical and numerical analyses, TUNE demonstrates versatility, robustness, and competitive power, offering a viable and reliable alternative for model-agnostic post-detection inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TUNE: Algorithm-Agnostic Inference after Changepoint Detection Jia, Yinxu Liu, Jixuan Wang, Guanghui Wang, Zhaojun Zou, Changliang Methodology Statistics Theory In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attention. Despite advancements through selective p-values, current methodologies often rely on stringent assumptions tied to specific changepoint models and detection algorithms, potentially compromising the accuracy of post-detection statistical inference. We introduce TUNE (Thresholding Universally and Nullifying change Effect), a novel algorithm-agnostic approach that uniformly controls error probabilities across detected changepoints. TUNE sets a universal threshold for multiple test statistics, applicable across a wide range of algorithms, and directly controls the family-wise error rate without the need for selective p-values. Through extensive theoretical and numerical analyses, TUNE demonstrates versatility, robustness, and competitive power, offering a viable and reliable alternative for model-agnostic post-detection inference. |
| title | TUNE: Algorithm-Agnostic Inference after Changepoint Detection |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2409.15676 |