Distribution and correlation-free two-sample test of high-dimensional means

DOI10.1214/19-AOS1848zbMath1454.62157arXiv1904.07416OpenAlexW3043189495MaRDI QIDQ2196222

Fang Yao, Kaijie Xue

Publication date: 28 August 2020

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

Full work available at URL: https://arxiv.org/abs/1904.07416

zbMATH Keywords

Kolmogorov distancepower functionmultiplier bootstraphigh-dimensional central limit theorem

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Asymptotic properties of parametric tests (62F05)

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A Permutation Test for Two-Sample Means and Signal Identification of High-dimensional Data ⋮ Projection-based high-dimensional sign test ⋮ Distribution/correlation-free test for two-sample means in high-dimensional functional data with eigenvalue decay relaxed ⋮ High-Dimensional MANOVA Via Bootstrapping and Its Application to Functional and Sparse Count Data ⋮ Linear Hypothesis Testing in Linear Models With High-Dimensional Responses ⋮ Likelihood ratio tests for elaborate covariance structures and for MANOVA models with elaborate covariance structures -- a review ⋮ Nonparametric Two-Sample Tests of High Dimensional Mean Vectors via Random Integration ⋮ A high‐dimensional inverse norm sign test for two‐sample location problems ⋮ An overview of tests on high-dimensional means ⋮ Recent developments in high-dimensional inference for multivariate data: parametric, semiparametric and nonparametric approaches ⋮ Novel multiplier bootstrap tests for high-dimensional data with applications to MANOVA ⋮ High-dimensional tests for mean vector: approaches without estimating the mean vector directly

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