Multiple change-point detection via a screening and ranking algorithm

Ning Hao, Yue Selena Niu, Heping Zhang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Let Y1; Yn be a sequence whose underlying mean is a step function with an unknown number of the steps and unknown change points. The detection of the change points, namely the positions where the mean changes, is an important problem in such fields as engineering, economics, climatology and bioscience. This problem has attracted a lot of attention in statistics, and a variety of solutions have been proposed and implemented. However, there is scant literature on the theoretical properties of those algorithms. Here, we investigate a recently developed algorithm called the Screening and Ranking algorithm (SaRa). We characterize the theoretical properties of SaRa and show its superiority over other commonly used algorithms. In particular, we develop a false discovery rate approach to the multiple change-point problem and show a strong sure coverage property for the SaRa.

Original languageEnglish (US)
Pages (from-to)1553-1572
Number of pages20
JournalStatistica Sinica
Volume23
Issue number4
DOIs
StatePublished - Jul 2013

Keywords

  • Change-point detection
  • Copy number variation
  • False discovery rate
  • High dimensional data
  • Screening and ranking algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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