Wild binary segmentation for multiple change-point detection. We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We assume that the number of change-points can increase to infinity with the sample size. Due to a certain random localisation mechanism, WBS works even for very short spacings between the change-points and/or very small jump magnitudes, unlike standard binary segmentation. On the other hand, despite its use of localisation, WBS does not require the choice of a window or span parameter, and does not lead to a significant increase in computational complexity. WBS is also easy to code. We propose two stopping criteria for WBS: one based on thresholding and the other based on what we term the `strengthened Schwarz information criterion’. We provide default recommended values of the parameters of the procedure and show that it offers very good practical performance in comparison with the state of the art. The WBS methodology is implemented in the R package wbs, available on CRAN. {par} In addition, we provide a new proof of consistency of binary segmentation with improved rates of convergence, as well as a corresponding result for WBS.

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  1. Fischer, Aurélie; Picard, Dominique: On change-point estimation under Sobolev sparsity (2020)
  2. Lu, Kang-Ping; Chang, Shao-Tung: Robust algorithms for multiphase regression models (2020)
  3. Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill: gfpop: an R Package for Univariate Graph-Constrained Change-point Detection (2020) arXiv
  4. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Univariate mean change point detection: penalization, CUSUM and optimality (2020)
  5. Zhuang, Dan; Liu, Youbo; Liu, Shuangzhe; Ma, Tiefeng; Ong, Seng-huat: A shape-based cutting and clustering algorithm for multiple change-point detection (2020)
  6. Zou, Changliang; Wang, Guanghui; Li, Runze: Consistent selection of the number of change-points via sample-splitting (2020)
  7. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  8. Chen, Zhanshou; Xu, Qiongyao; Li, Huini: Inference for multiple change points in heavy-tailed time series via rank likelihood ratio scan statistics (2019)
  9. Chiou, Jeng-Min; Chen, Yu-Ting; Hsing, Tailen: Identifying multiple changes for a functional data sequence with application to freeway traffic segmentation (2019)
  10. Fearnhead, Paul; Rigaill, Guillem: Changepoint detection in the presence of outliers (2019)
  11. Herrera Cortés, Silvia; Juárez Hernández, Bulmaro; Vázquez Guevara, Victor Hugo; Cruz Suárez, Hugo Adán: Parametric methodologies for detecting changes in maximum temperature of Tlaxco, Tlaxcala, México (2019)
  12. Li, Housen; Guo, Qinghai; Munk, Axel: Multiscale change-point segmentation: beyond step functions (2019)
  13. Li, Yingbo; Lund, Robert; Hewaarachchi, Anuradha: Multiple changepoint detection with partial information on changepoint times (2019)
  14. Raveendran, Nishanthi; Sofronov, Georgy: Identifying clusters in spatial data via sequential importance sampling (2019)
  15. Son, Won; Lim, Johan: Modified path algorithm of fused Lasso signal approximator for consistent recovery of change points (2019)
  16. Wang, Yunlong; Zou, Changliang; Wang, Zhaojun; Yin, Guosheng: Multiple change-points detection in high dimension (2019)
  17. Wan, Phyllis; Davis, Richard A.: Threshold selection for multivariate heavy-tailed data (2019)
  18. Zhao, Yan-Yong; Lin, Jin-Guan: Estimation and test of jump discontinuities in varying coefficient models with empirical applications (2019)
  19. Barigozzi, Matteo; Cho, Haeran; Fryzlewicz, Piotr: Simultaneous multiple change-point and factor analysis for high-dimensional time series (2018)
  20. Behr, Merle; Holmes, Chris; Munk, Axel: Multiscale blind source separation (2018)

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