Finite Sample Change Point Inference and Identification for High-Dimensional Mean Vectors

DOI10.1111/rssb.12406OpenAlexW3112218970MaRDI QIDQ5087399

Xiaohui Chen, Mengjia Yu

Publication date: 11 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://arxiv.org/abs/1711.08747

zbMATH Keywords

bootstrapCUSUMhigh-dimensional dataGaussian approximationchange point analysisbinary segmentation

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (13)

Inference for change points in high-dimensional data via selfnormalization ⋮ A robust bootstrap change point test for high-dimensional location parameter ⋮ Optimal multiple change-point detection for high-dimensional data ⋮ Adaptive Change Point Monitoring for High-Dimensional Data ⋮ Data-driven estimation of change-points with mean shift ⋮ Testing the martingale difference hypothesis in high dimension ⋮ Change-point testing for parallel data sets with FDR control ⋮ Central limit theorems for high dimensional dependent data ⋮ Change-point inference for high-dimensional heteroscedastic data ⋮ Change point detection for high dimensional data via kernel measure with application to human aging brain data ⋮ Detection of Multiple Structural Breaks in Large Covariance Matrices ⋮ High dimensional change point inference: recent developments and extensions ⋮ Robust inference for change points in high dimension

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