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.

References in zbMATH (referenced in 63 articles , 1 standard article )

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  1. Bouzebda, Salim; Ferfache, Anouar Abdeldjaoued: Asymptotic properties of (M)-estimators based on estimating equations and censored data in semi-parametric models with multiple change points (2021)
  2. Diop, Mamadou Lamine; Kengne, William: Piecewise autoregression for general integer-valued time series (2021)
  3. Kaul, Abhishek; Fotopoulos, Stergios B.; Jandhyala, Venkata K.; Safikhani, Abolfazl: Inference on the change point under a high dimensional sparse mean shift (2021)
  4. Li, Yuanbo; Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rongmao: Group orthogonal greedy algorithm for change-point estimation of multivariate time series (2021)
  5. Siddiqa, Hajra; Ali, Sajid; Shah, Ismail: Most recent changepoint detection in censored panel data (2021)
  6. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Optimal covariance change point localization in high dimensions (2021)
  7. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Optimal change point detection and localization in sparse dynamic networks (2021)
  8. Fang, Xiao; Li, Jian; Siegmund, David: Segmentation and estimation of change-point models: false positive control and confidence regions (2020)
  9. Fischer, Aurélie; Picard, Dominique: On change-point estimation under Sobolev sparsity (2020)
  10. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)
  11. Hahn, Georg; Fearnhead, Paul; Eckley, Idris A.: BayesProject: fast computation of a projection direction for multivariate changepoint detection (2020)
  12. Lu, Kang-Ping; Chang, Shao-Tung: Robust algorithms for multiphase regression models (2020)
  13. Ma, Lijing; Grant, Andrew J.; Sofronov, Georgy: Multiple change point detection and validation in autoregressive time series data (2020)
  14. Mohr, Maria; Selk, Leonie: Estimating change points in nonparametric time series regression models (2020)
  15. 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
  16. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Univariate mean change point detection: penalization, CUSUM and optimality (2020)
  17. Yang, Qing; Li, Yu-Ning; Zhang, Yi: Change point detection for nonparametric regression under strongly mixing process (2020)
  18. Zhuang, Dan; Liu, Youbo; Liu, Shuangzhe; Ma, Tiefeng; Ong, Seng-huat: A shape-based cutting and clustering algorithm for multiple change-point detection (2020)
  19. Zou, Changliang; Wang, Guanghui; Li, Runze: Consistent selection of the number of change-points via sample-splitting (2020)
  20. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv

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