Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection-Reference-Cited by-同舟云学术

Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection

Published:2020-03-02 Issue:4 Volume:49 Page:1027-1070
ISSN:1226-3192
Container-title:Journal of the Korean Statistical Society
language:en
Short-container-title:J. Korean Stat. Soc.

Author:

Fryzlewicz Piotr^ORCID

Funder

Engineering and Physical Sciences Research Council

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s42952-020-00060-x.pdf

Reference91 articles.

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3. Anastasiou, A., & Fryzlewicz, P. (2018b). IDetect: Detecting multiple generalized change-points by isolating single ones. https://CRAN.R-project.org/package=IDetect. R package version 1.0.

4. Andreou, E., & Ghysels, E. (2002). Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics, 17, 579–600.

5. Arlot, S. (2019). Minimal penalties and the slope heuristics: A survey. Journal de la Societe Française de Statistique, 160, 1–106.

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