Randomized incomplete \(U\)-statistics in high dimensions

DOI10.1214/18-AOS1773zbMath1435.62075arXiv1712.00771MaRDI QIDQ2284368

Xiaohui Chen, Kengo Kato

Publication date: 15 January 2020

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

Full work available at URL: https://arxiv.org/abs/1712.00771

zbMATH Keywords

bootstrapGaussian approximationdivide and conquerincomplete \(U\)-statisticsBernoulli samplingsampling with replacementrandomized inference

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Bootstrap, jackknife and other resampling methods (62F40) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (16)

Edgeworth expansions for network moments ⋮ Rates of convergence for random forests via generalized U-statistics ⋮ A robust bootstrap change point test for high-dimensional location parameter ⋮ High-dimensional central limit theorems for homogeneous sums ⋮ Distributed inference for two‐sample U‐statistics in massive data analysis ⋮ Inference for High-Dimensional Exchangeable Arrays ⋮ Testing the martingale difference hypothesis in high dimension ⋮ Central limit theorems for high dimensional dependent data ⋮ DESIGN BASED INCOMPLETE U-STATISTICS ⋮ High-dimensional central limit theorems by Stein's method ⋮ Approximating high-dimensional infinite-order \(U\)-statistics: statistical and computational guarantees ⋮ Randomized incomplete \(U\)-statistics in high dimensions ⋮ Gaussian approximations for high-dimensional non-degenerate \(U\)-statistics via exchangeable pairs ⋮ Improved central limit theorem and bootstrap approximations in high dimensions ⋮ On the consistency of incomplete U-statistics under infinite second-order moments ⋮ Stratified incomplete local simplex tests for curvature of nonparametric multiple regression

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