摘要：Changepoint detection is commonly approached by minimizing the sum of in-sample losses to quantify the model's overall fit across distinct data segments. However, we observe that flexible modeling techniques, particularly those involving hyperparameter tuning or model selection, often lead to inaccurate changepoint estimation due to biases that distort the target of in-sample loss minimization. To mitigate this issue, we propose a novel cross-fitting methodology that incorporates out-of-sample loss evaluations using independent samples separate from those used for model fitting. This approach ensures consistent changepoint estimation, contingent solely upon the models' predictive accuracy across nearly homogeneous data segments. Extensive numerical experiments demonstrate that our proposed cross-fitting strategy significantly enhances the reliability and adaptability of changepoint detection in complex scenarios.

 

个人简介：王光辉，南开大学统计与数据科学学院副教授。2018年博士毕业于南开大学。2021-2024年曾任华东师范大学统计学院副教授。研究兴趣包括变点检测和高维数据推断等。在统计学和机器学习领域的权威期刊与会议上，如JRSSB、AoS、JMLR，以及NeurIPS和AAAI等，发表多篇论文。