A backward procedure for change-point detection with applications to copy number variation detection

Seung Jun Shin, Yichao Wu, Ning Hao

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)366-385
Number of pages20
JournalCanadian Journal of Statistics
Issue number3
Publication statusPublished - 2020 Sept 1

Bibliographical note

Funding Information:
We thank two reviewers, an associate editor, and the editor for their most helpful comments that led to substantial improvements in the article. Shin is supported by grants from the National Research Foundation of Korea and Korea University. Wu and Hao are supported by the U.S. National Science Foundation. Hao is also supported by the Simons Foundation.

Publisher Copyright:
© 2020 Statistical Society of Canada / Société statistique du Canada


  • Backward detection
  • Short signal
  • copy number variation
  • mean change-point model
  • multiple change points

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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