It is increasingly the case with modern time series that many data sets of practical interest contain abrupt changes in structure. These changes may occur in complex characteristics such as the extremal dependence structure, and identifying such structural breaks remains a challenging problem. Many existing change point detection algorithms focus on changes in dependence across the entire distribution, rather than the tails, and approaches that are tailored to extremes typically make strict parametric assumptions or they are only applicable to bivariate data. We propose a nonparametric MOving sum-based approach for detecting multiple changes in the Pairwise Extremal Dependence (MOPED) of multivariate regularly varying data. To avoid the classical problem of threshold selection in the study of multivariate extremes, we further propose a multiscale, multi-threshold variant of MOPED that pools change point estimates across choices of the threshold and the bandwidth used in local estimation. Good performance of MOPED is illustrated in a simulation study, and we showcase its ability to identify subtle changes in tail dependence class in the absence of correlation changes. We further demonstrate the usefulness of MOPED by identifying changes in the extremal connectivity of electroencephalogram (EEG) signals of seizure-prone neonates.

翻译：现代时间序列数据中，许多实际应用的数据集常包含结构上的突变。这些变化可能发生在复杂特征中，如极值依赖结构，而识别此类结构突变仍是一个具有挑战性的问题。现有的许多变点检测算法主要关注整个分布上的依赖变化，而非尾部特征；而专门针对极值的方法通常需要严格的参数假设，或仅适用于二元数据。本文提出一种基于移动和的非参数方法，用于检测多元正则变化数据的成对极值依赖（MOPED）中的多重变点。为避免多元极值研究中经典的阈值选择问题，我们进一步提出MOPED的多尺度、多阈值变体，该方法通过聚合不同阈值和局部估计带宽选择下的变点估计结果。仿真研究验证了MOPED的良好性能，并展示了其在无相关性变化时识别尾部依赖类别细微变化的能力。通过识别易发癫痫新生儿脑电图（EEG）信号中极值连接性的变化，我们进一步证明了MOPED的实用价值。