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.

翻译：变点检测通常通过最小化样本内损失之和来量化模型在不同数据段上的整体拟合度。然而，我们观察到，灵活的建模技术——特别是涉及超参数调优或模型选择的方法——常因偏差导致样本内损失最小化的目标失真，从而产生不准确的变点估计。为缓解此问题，我们提出一种新颖的交叉拟合方法，该方法利用独立于模型拟合样本的额外数据评估样本外损失。该策略可确保变点估计的一致性，其前提仅要求模型在近似同质的数据段上具有预测准确性。大量数值实验表明，所提出的交叉拟合方法能显著提升复杂场景下变点检测的可靠性与适应性。