Gaussian approximation for high dimensional vector under physical dependence

DOI10.3150/17-BEJ939zbMath1419.62257MaRDI QIDQ1708978

Guang Cheng, Xianyang Zhang

Publication date: 27 March 2018

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1522051220

zbMATH Keywords

time seriesStein's methodhigh dimensionalityGaussian approximationphysical dependence measureSlepian interpolation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (20)

Inference for change points in high-dimensional data via selfnormalization ⋮ Non-asymptotic properties of spectral decomposition of large Gram-type matrices and applications ⋮ A rank-based high-dimensional test for equality of mean vectors ⋮ Sequential change point detection in high dimensional time series ⋮ Inference for High-Dimensional Exchangeable Arrays ⋮ Nearly optimal central limit theorem and bootstrap approximations in high dimensions ⋮ Testing the martingale difference hypothesis in high dimension ⋮ Central limit theorems for high dimensional dependent data ⋮ Second- and higher-order Gaussian anticoncentration inequalities and error bounds in Slepian's comparison theorem ⋮ Estimation and inference for precision matrices of nonstationary time series ⋮ Hypothesis testing for high-dimensional time series via self-normalization ⋮ Uniform nonparametric inference for time series ⋮ High-dimensional central limit theorems by Stein's method ⋮ Lasso Inference for High-Dimensional Time Series ⋮ On nonparametric inference for spatial regression models under domain expanding and infill asymptotics ⋮ Relevant change points in high dimensional time series ⋮ Time series modeling on dynamic networks ⋮ Bootstrap based inference for sparse high-dimensional time series models ⋮ Boosting high dimensional predictive regressions with time varying parameters ⋮ Improved central limit theorem and bootstrap approximations in high dimensions

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