Beyond Gaussian approximation: bootstrap for maxima of sums of independent random vectors
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Publication:1996788
DOI10.1214/20-AOS1946zbMath1461.62042arXiv1705.09528OpenAlexW3113009545MaRDI QIDQ1996788
Publication date: 26 February 2021
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1705.09528
comparison theoremrandom matrixmultiplier bootstrapsimultaneous statistical inferenceanticoncentrationempirical bootstrapLindeberg interpolation
Random matrices (probabilistic aspects) (60B20) Bootstrap, jackknife and other resampling methods (62F40) Approximations to statistical distributions (nonasymptotic) (62E17) Paired and multiple comparisons; multiple testing (62J15)
Related Items (12)
Notes on the dimension dependence in high-dimensional central limit theorems for hyperrectangles ⋮ A sharp lower-tail bound for Gaussian maxima with application to bootstrap methods in high dimensions ⋮ High-dimensional central limit theorems for homogeneous sums ⋮ 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 ⋮ StarTrek: combinatorial variable selection with false discovery rate control ⋮ Second- and higher-order Gaussian anticoncentration inequalities and error bounds in Slepian's comparison theorem ⋮ Gaussian approximations for high-dimensional non-degenerate \(U\)-statistics via exchangeable pairs ⋮ Central limit theorem and bootstrap approximation in high dimensions: near \(1/\sqrt{n}\) rates via implicit smoothing ⋮ Improved central limit theorem and bootstrap approximations in high dimensions
Uses Software
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