R package not. Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following ’deterministic signal + noise’ model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
- Descloux, Pascaline; Sardy, Sylvain: Model selection with Lasso-zero: adding straw to the haystack to better find needles (2021)
- Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (2021)
- Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Ranking-based variable selection for high-dimensional data (2020)
- Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
- Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
- Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
- Fryzlewicz, Piotr: Wild binary segmentation for multiple change-point detection (2014)