A Unified Data-Adaptive Framework for High Dimensional Change Point Detection

DOI10.1111/rssb.12375OpenAlexW3034606471MaRDI QIDQ5087162

Bin Liu, Yu Feng Liu, Cheng Zhou, Xin Sheng Zhang

Publication date: 8 July 2022

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/rssb.12375

zbMATH Keywords

\(U\)-statisticshigh dimensionsminimax optimalitychange point detectiondata-adaptive testsmultiplier bootstrap and Gaussian approximation

Mathematics Subject Classification ID

Statistics (62-XX)

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A Unified Framework for Change Point Detection in High-Dimensional Linear Models ⋮ A weighted U-statistic based change point test for multivariate time series ⋮ Change-point testing for parallel data sets with FDR control ⋮ High dimensional change point inference: recent developments and extensions ⋮ Robust inference for change points in high dimension ⋮ A computationally efficient and flexible algorithm for high dimensional mean and covariance matrix change point models

Uses Software

• R