Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
arXiv:2006.12806 · doi:10.1007/s42952-020-00077-2
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.
To appear in the Journal of the Korean Statistical Society