wbs

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 92 articles , 1 standard article )

Showing results 21 to 40 of 92.
Sorted by year (citations)
  1. Shaochuan, Lu: Bayesian multiple changepoint detection for stochastic models in continuous time (2021)
  2. Siddiqa, Hajra; Ali, Sajid; Shah, Ismail: Most recent changepoint detection in censored panel data (2021)
  3. Tan, Changchun; Hu, Junying; Wu, Yuehua: Detection of multiple change-points in the scale parameter of a gamma distributed sequence based on reversible jump MCMC (2021)
  4. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Optimal covariance change point localization in high dimensions (2021)
  5. Wang, Daren; Yu, Yi; Rinaldo, Alessandro: Optimal change point detection and localization in sparse dynamic networks (2021)
  6. Zhao, Zifeng; Yau, Chun Yip: Alternating pruned dynamic programming for multiple epidemic change-point estimation (2021)
  7. Banerjee, Moulinath: Discussion of `Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection’ (2020)
  8. Cho, Haeran; Kirch, Claudia: Discussion of `Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection’ (2020)
  9. Fang, Xiao; Li, Jian; Siegmund, David: Segmentation and estimation of change-point models: false positive control and confidence regions (2020)
  10. Fischer, Aurélie; Picard, Dominique: On change-point estimation under Sobolev sparsity (2020)
  11. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  12. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection -- rejoinder (2020)
  13. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)
  14. Hahn, Georg; Fearnhead, Paul; Eckley, Idris A.: BayesProject: fast computation of a projection direction for multivariate changepoint detection (2020)
  15. Kovács, Solt; Li, Housen; Bühlmann, Peter: Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020) (2020)
  16. Lu, Kang-Ping; Chang, Shao-Tung: Robust algorithms for multiphase regression models (2020)
  17. Lund, Robert; Shi, Xueheng: Short communication: detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  18. Ma, Lijing; Grant, Andrew J.; Sofronov, Georgy: Multiple change point detection and validation in autoregressive time series data (2020)
  19. Mohr, Maria; Selk, Leonie: Estimating change points in nonparametric time series regression models (2020)
  20. Tickle, S. O.; Eckley, I. A.; Fearnhead, P.; Haynes, K.: Parallelization of a common changepoint detection method (2020)