Univariate mean change point detection: penalization, CUSUM and optimality
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Publication:2180083
DOI10.1214/20-EJS1710zbMath1442.62097arXiv1810.09498OpenAlexW3019220537MaRDI QIDQ2180083
Daren Wang, Alessandro Rinaldo, Yi Yu
Publication date: 13 May 2020
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.09498
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On optimal segmentation and parameter tuning for multiple change-point detection and inference ⋮ Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection ⋮ Short communication: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection ⋮ Optimal multiple change-point detection for high-dimensional data ⋮ Detecting Multiple Change Points: The PULSE Criterion ⋮ Moving Sum Data Segmentation for Stochastic Processes Based on Invariance ⋮ Bayesian Change Point Detection with Spike-and-Slab Priors ⋮ Optimal change-point detection and localization ⋮ Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models ⋮ Inference on the change point under a high dimensional sparse mean shift ⋮ Unnamed Item ⋮ Two-stage data segmentation permitting multiscale change points, heavy tails and dependence ⋮ Localising change points in piecewise polynomials of general degrees ⋮ Optimal nonparametric change point analysis ⋮ Optimal covariance change point localization in high dimensions ⋮ A Note on Online Change Point Detection ⋮ Optimal change point detection and localization in sparse dynamic networks ⋮ Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes ⋮ Minimax rates in sparse, high-dimensional change point detection ⋮ Consistency of a range of penalised cost approaches for detecting multiple changepoints
Uses Software
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