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