Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)
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Author / Producer
Date
2020-12
Publication Type
Other Journal Item
ETH Bibliography
yes
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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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Publication status
published
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Book title
Journal / series
Volume
49 (4)
Pages / Article No.
1081 - 1089
Publisher
Springer
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Break points; Fast computation; Model selection; Reproducibility; Seeded binary segmentation; Steepest drop to low levels; Variance estimation; Wild binary segmentation 2
Organisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
Notes
Funding
786461 - Statistics, Prediction and Causality for Large-Scale Data (EC)