Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2020.
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- 2022Random Forests for Change Point Detectioncited by 14
- 2022Random Forests for Change Point Detection10 May 2022