Short communication: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s42952-020-00081-6.pdf
Reference13 articles.
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, 81(3), 649–672.
2. Burkard, R., Dell’Amico, M., & Martello, S. (2012). Assignment Problems (revised ed., Vol. 106). Philadelphia: Society of Industrial and Applied Mathematics.
3. Chakar, S., Lebarbier, E., Lévy-Leduc, C., & Robin, S. (2017). A robust approach for estimating change-points in the mean of an $$\text{ AR }(1)$$ process. Bernoulli, 23(2), 1408–1447.
4. Davis, R. A., Lee, T. C. M., & Rodriguez-Yam, G. A. (2006). Structural break estimation for nonstationary time series models. Journal of the American Statistical Association, 101(473), 223–239.
5. Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.
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