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Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection—rejoinder
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 i...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Springer Singapore
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493064/ https://www.ncbi.nlm.nih.gov/pubmed/32952406 http://dx.doi.org/10.1007/s42952-020-00085-2 |