Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)

by   Solt Kovács, et al.

In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives. Furthermore, we investigate a novel estimator of the noise level, which improves many existing model selection procedures (including the steepest drop to low levels), particularly for challenging frequent change point scenarios with low signal-to-noise ratios.



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  • Baranowski et al. (2019) Baranowski, R., Chen, Y., and 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.
  • Du et al. (2016) Du, C., Kao, C.-L. M., and Kou, S. C. (2016). Stepwise signal extraction via marginal likelihood. Journal of the American Statistical Association, 111(513):314–330.
  • Fearnhead and Rigaill (2020) Fearnhead, P. and Rigaill, G. (2020). Relating and comparing methods for detecting changes in mean. To appear in Stat, e291.
  • Fryzlewicz (2014) Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6):2243–2281.
  • Fryzlewicz (2020) Fryzlewicz, P. (2020). Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. Journal of the Korean Statistical Society.
  • Hall et al. (1990) Hall, P., Kay, J. W., and Titterington, D. M. (1990). Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika, 77(3):521–528.
  • Killick et al. (2012) Killick, R., Fearnhead, P., and Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500):1590–1598.
  • Kovács et al. (2020) Kovács, S., Li, H., Bühlmann, P., and Munk, A. (2020). Seeded binary segmentation: a general methodology for fast and optimal change point detection. arXiv:2002.06633.
  • Kovács et al. (2020) Kovács, S., Li, H., Haubner, L., Bühlmann, P., and Munk, A. (2020). Optimistic search strategies: change point detection without full grid search. Working paper.
  • Li et al. (2016) Li, H., Munk, A., and Sieling, H. (2016). FDR-control in multiscale change-point segmentation. Electronic Journal of Statistics, 10(1):918–959.
  • Londschien et al. (2019) Londschien, M., Kovács, S., and Bühlmann, P. (2019). Change point detection for graphical models in the presence of missing values. To appear in the Journal of Computational and Graphical Statistics, arXiv:1907.05409.
  • Vostrikova (1981) Vostrikova, L. Y. (1981). Detecting ‘disorder’ in multidimensional random processes. Soviet Mathematics Doklady, 24:55–59.
  • Yao (1988) Yao, Y.-C. (1988).

    Estimating the number of change-points via Schwarz’ criterion.

    Statistics & Probability Letters

    , 6(3):181–189.