Change‐point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change‐point detection method, a backward procedure, which is not only fast and simple enough to exploit high‐dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change‐point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods, especially when the true signal is short. The Canadian Journal of Statistics 48: 366–385; 2020 © 2020 Statistical Society of Canada

中文翻译：

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变更点检测的后向过程，以及复制数量变化检测的应用

更改点检测最近在分析阵列或测序数据以检测拷贝数变异（CNV）方面​​引起了广泛关注。在这样的应用中，真实信号通常非常短，并且掩埋在较长的数据序列中，这使得有效，准确地识别变化具有挑战性。在本文中，我们提出了一种新的变更点检测方法，即反向过程，该方法不仅足够快速，简单，足以利用高维数据，而且在检测短信号方面也表现出色。尽管受CNV检测的推动，但后向过程通常适用于各种科学应用中出现的各种变化点问题。模拟和实际CNV数据都表明，反向检测相对于其他竞争方法具有明显的优势，加拿大统计杂志48：366-385；加拿大统计局。2020©2020加拿大统计学会