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

Abstract

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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Acknowledgements

Solt Kovács and Peter Bühlmann have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 786461 CausalStats - ERC-2017-ADG). Housen Li gratefully acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2067/1-390729940.

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Correspondence to Solt Kovács.

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Kovács, S., Li, H. & Bühlmann, P. Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020). J. Korean Stat. Soc. (2020). https://doi.org/10.1007/s42952-020-00077-2

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Keywords

  • Break points
  • Fast computation
  • Model selection
  • Reproducibility
  • Seeded binary segmentation
  • Steepest drop to low levels
  • Variance estimation
  • Wild binary segmentation 2