Model-X Change-Point Detection of Conditional Distribution
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866915721557049344 |
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| author | Dong, Zhuofan Huang, Yiwen Dong, Yan Yan, Mengying Tian, Ziye Hong, Chuan Zhou, Doudou Liu, Molei |
| author_facet | Dong, Zhuofan Huang, Yiwen Dong, Yan Yan, Mengying Tian, Ziye Hong, Chuan Zhou, Doudou Liu, Molei |
| contents | The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-point detection on the outcome models to guide model retraining and adjustments. However, inferring the change point of conditional models is more prone to loss of validity or power than classic detection problems for marginal distributions. This is due to both the temporal covariate shift and the complexity of the outcome model. Also, the existing method of conditional change points detection both have many limitations including linear assumption and low dimension prerequisite which sometimes is not suitable for real world application. To address these challenges, we propose a novel Model-X changE-point detectioN of conditional Distribution (MEND) method computationally enhanced with distillation function for simultaneous change-point detection and localization of the conditional outcome model. We extend and combine our model with neural network to accommodate complex nonlinear and high dimensional situation, which is proved to be valid in both simulation and real data. Theoretical validity of the proposed method is justified. Extensive simulation studies and two real-world examples demonstrate the statistical effectiveness and computational scalability of our method as well as its significant improvements over existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-X Change-Point Detection of Conditional Distribution Dong, Zhuofan Huang, Yiwen Dong, Yan Yan, Mengying Tian, Ziye Hong, Chuan Zhou, Doudou Liu, Molei Methodology The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-point detection on the outcome models to guide model retraining and adjustments. However, inferring the change point of conditional models is more prone to loss of validity or power than classic detection problems for marginal distributions. This is due to both the temporal covariate shift and the complexity of the outcome model. Also, the existing method of conditional change points detection both have many limitations including linear assumption and low dimension prerequisite which sometimes is not suitable for real world application. To address these challenges, we propose a novel Model-X changE-point detectioN of conditional Distribution (MEND) method computationally enhanced with distillation function for simultaneous change-point detection and localization of the conditional outcome model. We extend and combine our model with neural network to accommodate complex nonlinear and high dimensional situation, which is proved to be valid in both simulation and real data. Theoretical validity of the proposed method is justified. Extensive simulation studies and two real-world examples demonstrate the statistical effectiveness and computational scalability of our method as well as its significant improvements over existing methods. |
| title | Model-X Change-Point Detection of Conditional Distribution |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.12023 |