Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)

DOI10.1007/S42952-020-00077-2zbMath1485.62122arXiv2006.12806OpenAlexW3086604801MaRDI QIDQ2131954

Housen Li, Solt Kovács

Publication date: 27 April 2022

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.12806

zbMATH Keywords

model selectionvariance estimationfast computationreproducibilitybreak pointsseeded binary segmentationsteepest drop to low levelswild binary segmentation 2

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: hypothesis testing (62M07)

Uses Software

• wbs
• FDRSeg
• hdcd

Cites Work

• Unnamed Item
• FDR-control in multiscale change-point segmentation
• Wild binary segmentation for multiple change-point detection
• Estimating the number of change-points via Schwarz' criterion
• Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection
• Asymptotically optimal difference-based estimation of variance in nonparametric regression
• Optimal Detection of Changepoints With a Linear Computational Cost
• Change-Point Detection for Graphical Models in the Presence of Missing Values
• Narrowest-Over-Threshold Detection of Multiple Change Points and Change-Point-Like Features

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