Inference for change points in high-dimensional data via selfnormalization

DOI10.1214/21-AOS2127zbMath1486.62246arXiv1905.08446OpenAlexW3192985938MaRDI QIDQ2131255

Stanislav Volgushev, Runmin Wang, Changbo Zhu, Xiao-Feng Shao

Publication date: 25 April 2022

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.08446

zbMATH Keywords

time seriesCUSUMsegmentationstructural breakU-statisticselfnormalization

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Non-Markovian processes: hypothesis testing (62M07)

Related Items (8)

Change-point detection based on weighted two-sample U-statistics ⋮ Optimal multiple change-point detection for high-dimensional data ⋮ Sequential change point detection in high dimensional time series ⋮ Adaptive Inference for Change Points in High-Dimensional Data ⋮ Kolmogorov-Smirnov type testing for structural breaks: a new adjusted-range based self-normalization approach ⋮ Change-point inference for high-dimensional heteroscedastic data ⋮ Two-sample and change-point inference for non-Euclidean valued time series ⋮ Robust inference for change points in high dimension

Uses Software

• wbs

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