标题： 复杂模型中的变点检测：需要交叉拟合 显示英文标题

标题： Changepoint Detection in Complex Models: Cross-Fitting Is Needed

摘要： 变点检测通常通过最小化样本内损失的总和来衡量模型在整个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，特别是那些涉及超参数调整或模型选择的技术，由于会扭曲样本内损失最小化的目标而导致变点估计不准确。为了解决这个问题，我们提出了一种新的交叉拟合方法，该方法结合了独立样本的样本外损失评估，这些样本与用于模型拟合的样本分开。这种方法确保了一致的变点估计，仅依赖于模型在几乎同质的数据段上的预测准确性。大量的数值实验表明，我们提出的交叉拟合策略显著提高了复杂场景下变点检测的可靠性和适应性。 显示英文摘要

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.