Discussion of ‘Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection’

This is a preview of subscription content, log in to check access.


  1. Baranowski, R., Chen, Y., & Fryzlewicz, P.. (2019). Narrowest-over-threshold detection of multiple change points and change-point-like features. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(3), 649–672.

    MathSciNet  Article  Google Scholar 

  2. Fryzlewicz, P. (2020). Detecting possibly frequent change-points: Wild binary segmentation 2 and steepest-drop model selection. Journal of the Korean Statistical Society. https://doi.org/10.1007/s42952-020-00060-x.

    Article  Google Scholar 

  3. Fryzlewicz, P., et al. (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.

    MathSciNet  Article  Google Scholar 

  4. Kovács, S., Li, H., Bühlmann, P., & Munk, A. (2020). Seeded binary segmentation: A general methodology for fast and optimal change point detection. arXiv preprint arXiv:2002.06633. Accessed 1 June 2020.

  5. Lan, Y., Banerjee, M., Michailidis, G., et al. (2009). Change-point estimation under adaptive sampling. The Annals of Statistics, 37(4), 1752–1791.

    MathSciNet  Article  Google Scholar 

  6. Lu, Z., Banerjee, M., & Michailidis, G. (2020.) Intelligent sampling for multiple change-points in exceedingly long time series with rate guarantees. arXiv preprint arXiv:1710.07420. Accessed 1 June 2020.

  7. Mallik, A., Banerjee, M., & Michailidis, G. (2020). M-estimation in multistage sampling procedures. Sankhya A,. https://doi.org/10.1007/s13171-019-00194-z.

    MathSciNet  Article  Google Scholar 

  8. Tang, R., Banerjee, M., Michailidis, G., & Mankad, S. (2015). Two-stage plans for estimating the inverse of a monotone function. Technometrics, 57(3), 395–407.

    MathSciNet  Article  Google Scholar 

  9. Wang, D., Yu, Y., & Rinaldo, A. (2018). Optimal change point detection and localization in sparse dynamic networks. arXiv preprint arXiv:1809.09602. Accessed 1 June 2020.

Download references

Author information



Corresponding author

Correspondence to Moulinath Banerjee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article refers to the article available online at https://doi.org/10.1007/s42952-020-00060-x.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Banerjee, M. Discussion of ‘Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection’. J. Korean Stat. Soc. (2020). https://doi.org/10.1007/s42952-020-00079-0

Download citation