We propose a general methodology Reliever for fast and reliable changepoint detection when the model fitting is costly. Instead of fitting a sequence of models for each potential search interval, Reliever employs a substantially reduced number of proxy/relief models that are trained on a predetermined set of intervals. This approach can be seamlessly integrated with state-of-the-art changepoint search algorithms. In the context of high-dimensional regression models with changepoints, we establish that the Reliever, when combined with an optimal search scheme, achieves estimators for both the changepoints and corresponding regression coefficients that attain optimal rates of convergence, up to a logarithmic factor. Through extensive numerical studies, we showcase the ability of Reliever to rapidly and accurately detect changes across a diverse range of parametric and nonparametric changepoint models.

翻译：我们提出一种通用方法Reliever，用于在模型拟合成本高昂时实现快速可靠的变化点检测。与对每个潜在搜索区间依次拟合模型不同，Reliever采用数量大幅减少的代理/缓解模型，这些模型基于一组预定义区间进行训练。该方法能够无缝集成当前最先进的变化点搜索算法。在含变化点的高维回归模型情境下，我们证明：当与最优搜索方案结合时，Reliever对变化点及相应回归系数的估计量可达到最优收敛速度（相差不超过对数因子）。通过大量数值实验，我们展示了Reliever在各类参数与非参数变化点模型中快速精准检测变化的能力。