Asymptotic distribution-free changepoint detection for data with repeated observations
针对重复观测数据，扩展了基于图的扫描统计量变点检测框架，通过平均或并集所有可能最优图来控制第一类错误，适用于高维和非欧几里得数据序列，并在动态网络序列中验证了方法。
Summary A nonparametric framework for changepoint detection, based on scan statistics utilizing graphs that represent similarities among observations, is gaining attention owing to its flexibility and good performance for high-dimensional and non-Euclidean data sequences. However, this graph-based framework faces challenges when there are repeated observations in the sequence, which is often the case for discrete data such as network data. In this article we extend the graph-based framework to solve this problem by averaging or taking the union of all possible optimal graphs resulting from repeated observations. We consider both the single-changepoint alternative and the changed-interval alternative, and derive analytical formulas to control the Type I error for the new methods, making them readily applicable to large datasets. The extended methods are illustrated on an application in detecting changes in a sequence of dynamic networks over time. All proposed methods are implemented in an $\texttt{R}$ package $\texttt{gSeg}$ available on CRAN.