ART：具有有限样本保证的无分布、模型无关变点检测方法
ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees
AI总结：
本文提出无分布、模型无关的变点检测框架ART，通过对称评分、排序与多尺度聚合实现精确第一类错误控制及检测后推断。
摘要：
我们提出了ART，这是一个无分布且模型无关的变点检测框架，并提供有限样本保证。ART通过一个对称函数将独立观测转换为实值分数，确保在不存在变点时具有可交换性。随后对这些分数进行排序和聚合，以检测分布变化。所得检验能够精确控制第一类错误，且不依赖于特定的分布或模型假设。此外，ART可无缝扩展到多尺度情形，实现稳健的多变点估计以及具有有限样本错误率控制的检测后推断。通过对分数进行局部排序并跨多个预先指定的区间执行聚合，ART能够识别变点区间并改进后续推断，同时保持其无分布和模型无关的特性。这种适应性使ART成为现代变点分析的可靠且通用的工具，尤其适用于高维数据环境以及利用机器学习方法的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
