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 )

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  1. Anastasiou, Andreas; Fryzlewicz, Piotr: Detecting multiple generalized change-points by isolating single ones (2022)
  2. Belcaid, A.; Belkbir, H.: Constrained energy variation for change point detection (2022)
  3. Korkas, Karolos K.: Ensemble binary segmentation for irregularly spaced data with change-points (2022)
  4. Liu, Bin; Zhang, Xinsheng; Liu, Yufeng: High dimensional change point inference: recent developments and extensions (2022)
  5. Rajaganapathy, Sivaraman; Melbourne, James; Salapaka, Murti V.: Change detection using an iterative algorithm with guarantees (2022)
  6. Shi, Xuesheng; Gallagher, Colin; Lund, Robert; Killick, Rebecca: A comparison of single and multiple changepoint techniques for time series data (2022)
  7. Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque: Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data (2022) not zbMATH
  8. Wang, Runmin; Zhu, Changbo; Volgushev, Stanislav; Shao, Xiaofeng: Inference for change points in high-dimensional data via selfnormalization (2022)
  9. Azadeh Khaleghi, Lukas Zierahn: PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series (2021) arXiv
  10. 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)
  11. Degras, David: Sparse group fused Lasso for model segmentation: a hybrid approach (2021)
  12. Descloux, Pascaline; Sardy, Sylvain: Model selection with Lasso-zero: adding straw to the haystack to better find needles (2021)
  13. Diop, Mamadou Lamine; Kengne, William: Piecewise autoregression for general integer-valued time series (2021)
  14. Gibberd, A.; Roy, S.: Consistent multiple changepoint estimation with fused Gaussian graphical models (2021)
  15. Kang, Sang Gil; Lee, Woo Dong; Kim, Yongku: Bayesian multiple change-points detection in a normal model with heterogeneous variances (2021)
  16. Kaul, Abhishek; Fotopoulos, Stergios B.; Jandhyala, Venkata K.; Safikhani, Abolfazl: Inference on the change point under a high dimensional sparse mean shift (2021)
  17. Li, Yuanbo; Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rongmao: Group orthogonal greedy algorithm for change-point estimation of multivariate time series (2021)
  18. Londschien, Malte; Kovács, Solt; Bühlmann, Peter: Change-point detection for graphical models in the presence of missing values (2021)
  19. Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (2021)
  20. Qian, Hangwei; Pan, Sinno Jialin; Miao, Chunyan: Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions (2021)

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