ArticleJournal of the Korean Statistical Society2020
Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection-rejoinder.
Article in Journal of the Korean Statistical Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Retrospective and multifactorial single-cell profiling reveals sequential chromatin reorganization during X inactivation.Nature cell biology · 2025Article
- An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals.Scientific reports · 2025Article
- Long-term trends in water level fluctuations in lowland lakes in central Europe (Northern Poland).Scientific reports · 2025Article
- Detection and estimation of multiple transient changes.Journal of applied statistics · 2023Article
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Abstract
Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is "Wild Binary Segmentation 2" (WBS2), a recursive algorithm for producing what we call a 'complete' solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing
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