Narrowest Significance Pursuit: Inference for Multiple Change-Points in Linear Models

DOI10.1080/01621459.2023.2211733MaRDI QIDQ6567956

Piotr Fryzlewicz

Publication date: 5 July 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

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Cites Work

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• A MOSUM procedure for the estimation of multiple random change points
• FDR-control in multiscale change-point segmentation
• Multiple Testing of Local Extrema for Detection of Change Points
• Wild binary segmentation for multiple change-point detection
• Limiting distribution for the maximal standardized increment of a random walk
• Invariance principle under self-normalization for nonidentically distributed random variables
• Exact post-selection inference for the generalized Lasso path
• Minimax estimation of sharp change points
• Using the generalized likelihood ratio statistic for sequential detection of a change-point
• Detecting multiple generalized change-points by isolating single ones
• Segmentation and estimation of change-point models: false positive control and confidence regions
• Multiple change-point detection via a screening and ranking algorithm
• Estimating and Testing Linear Models with Multiple Structural Changes
• Detection with the scan and the average likelihood ratio
• Multiscale change point detection for dependent data
• Narrowest-Over-Threshold Detection of Multiple Change Points and Change-Point-Like Features
• Heterogeneous Change Point Inference
• Multiscale Change Point Inference
• Post‐selection inference for changepoint detection algorithms with application to copy number variation data
• Testing for a change in mean after changepoint detection

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