High Dimensional Change Point Inference: Recent Developments and Extensions
High Dimensional Change Point Inference: Recent Developments and Extensions
复制标题
高维变点推断：最新发展和扩展
DOI：
10.1016/j.jmva.2021.104833
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发表时间：
2022
影响因子：
1.6
通讯作者：
Liu, B.
中科院分区：
文献类型：
--
作者：
Liu, B.
变化点分析的目的是检测数据序列中的结构变化。自20世纪50年代引入以来，它一直是一个活跃的研究领域。然而，在现代统计应用中，从经济、金融到遗传学和工程等领域中，高通量、维度不断增加的数据无处不在。对于这些问题，早期的工作通常不再适用。因此，测试高维数据序列的变化点问题一直是一项重要而又具有挑战性的任务。在本文中，我们首先关注最多一个变化点的模型，回顾了高维平均向量变化点测试的最新技术，并比较了它们的理论性质。在此基础上，我们综述了一般高维参数在平均向量之外的一些扩展，以及测试高维中多个变化点的策略。最后，对未来可能的研究方向进行了讨论。
Change point analysis aims to detect structural changes in a data sequence. It has always been an active research area since it was introduced in the 1950s. In modern statistical applications, however, high-throughput data with increasing dimensions are ubiquitous in fields ranging from economics, finance to genetics and engineering. For those problems, the earlier works are typically no longer applicable. As a result, the problem of testing a change point for high dimensional data sequences has been an important yet challenging task. In this paper, we first focus on models for at most one change point, and review recent state-of-art techniques for change point testing of high dimensional mean vectors and compare their theoretical properties. Based on that, we provide a survey of some extensions to general high dimensional parameters beyond mean vectors as well as strategies for testing multiple change points in high dimensions. Finally, we discuss some open problems for possible future research directions.