Testing and estimating change-points in the covariance matrix of a high-dimensional time series

DOI10.1016/j.jmva.2019.104582zbMath1445.62239arXiv1912.04677OpenAlexW2999233900WikidataQ126333945 ScholiaQ126333945MaRDI QIDQ2306269

Ansgar Steland

Publication date: 20 March 2020

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.04677

zbMATH Keywords

spatial statisticsprojectionstrong approximationdata sciencehigh-dimensional statisticschange pointspiked covarianceVARMA processesCUSUM transform

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Measures of association (correlation, canonical correlation, etc.) (62H20) Strong limit theorems (60F15)

Related Items (4)

Functional Estimation and Change Detection for Nonstationary Time Series ⋮ Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices ⋮ Detecting changes in the second moment structure of high-dimensional sensor-type data in a K-sample setting ⋮ High dimensional change point inference: recent developments and extensions

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