论文标题
实用且强大的基于内核的更改点检测
Practical and powerful kernel-based change-point detection
论文作者
论文摘要
变更点分析在各个领域都起着重要作用，以揭示一系列观测顺序分布的差异。尽管已经提出了许多用于高维数据的算法，但由于难以控制错误的发现和平庸的性能，基于内核的方法尚未得到很好的探索。在本文中，我们提出了一个新的基于内核的框架，该框架利用高维数据的重要数据模式来增强功率。提出了针对新统计数据的重要性的分析近似，并根据渐近结果得出了快速测试，为大型数据集提供了简单的现成工具。与其他最先进的方法相比，新的测试显示出多种替代方案的表现。我们通过分析电话通话网络数据来说明这些新方法。所有提出的方法均在r软件包Kerseg中实现。
Change-point analysis plays a significant role in various fields to reveal discrepancies in distribution in a sequence of observations. While a number of algorithms have been proposed for high-dimensional data, kernel-based methods have not been well explored due to difficulties in controlling false discoveries and mediocre performance. In this paper, we propose a new kernel-based framework that makes use of an important pattern of data in high dimensions to boost power. Analytic approximations to the significance of the new statistics are derived and fast tests based on the asymptotic results are proposed, offering easy off-the-shelf tools for large datasets. The new tests show superior performance for a wide range of alternatives when compared with other state-of-the-art methods. We illustrate these new approaches through an analysis of a phone-call network data. All proposed methods are implemented in an R package KerSeg.
