论文标题
基于新排名方案
Asymptotic Distribution-free Change-point Detection for Modern Data Based on a New Ranking Scheme
论文作者
论文摘要
变更点检测（CPD）涉及确定一系列独立观察序列中的分布变化。在非参数方法中，基于等级的方法由于其稳健性和有效性而具有吸引力，并且已经对单变量数据进行了广泛的研究。但是，对于高维或非欧盟数据，它们的探索尚未得到很好的探索。本文提出了一种新方法，该方法是由图更换点检测（RING-CPD）引起的，该方法利用图形诱导的等级来处理高维和非欧盟岛数据。新方法在零假设下渐近地分配，并且为易于I型误差控制提供了分析$ P $值近似。仿真研究表明，RING-CPD有效地检测了各种替代方案的变化点，并且对重尾分布和异常值也很强。通过在功能连接网络数据集，数字图像的变化以及纽约市出租车数据集中的癫痫发作的检测中检测到新方法。
Change-point detection (CPD) involves identifying distributional changes in a sequence of independent observations. Among nonparametric methods, rank-based methods are attractive due to their robustness and effectiveness and have been extensively studied for univariate data. However, they are not well explored for high-dimensional or non-Euclidean data. This paper proposes a new method, Rank INduced by Graph Change-Point Detection (RING-CPD), which utilizes graph-induced ranks to handle high-dimensional and non-Euclidean data. The new method is asymptotically distribution-free under the null hypothesis, and an analytic $p$-value approximation is provided for easy type-I error control. Simulation studies show that RING-CPD effectively detects change points across a wide range of alternatives and is also robust to heavy-tailed distribution and outliers. The new method is illustrated by the detection of seizures in a functional connectivity network dataset, changes of digit images, and travel pattern changes in the New York City Taxi dataset.
