Hypothesis testing for high-dimensional time series via self-normalization

DOI10.1214/19-AOS1904zbMath1464.62307OpenAlexW3087337278MaRDI QIDQ2215757

Runmin Wang, Xiao-Feng Shao

Publication date: 14 December 2020

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

Full work available at URL: https://projecteuclid.org/euclid.aos/1600480930

zbMATH Keywords

nonlinear time seriesmartingale approximationhigh-dimensional inferenceU-statisticself-normalizationpivotal limit

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) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Martingales with continuous parameter (60G44)

Related Items (8)

Inference for change points in high-dimensional data via selfnormalization ⋮ A rank-based high-dimensional test for equality of mean vectors ⋮ Optimal multiple change-point detection for high-dimensional data ⋮ Adaptive Inference for Change Points in High-Dimensional Data ⋮ Kolmogorov-Smirnov type testing for structural breaks: a new adjusted-range based self-normalization approach ⋮ Two-sample and change-point inference for non-Euclidean valued time series ⋮ Inference on the change point under a high dimensional sparse mean shift ⋮ Adjusted-range self-normalized confidence interval construction for censored dependent data

Uses Software

• fdahotelling

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