A \(U\)-statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens-Fisher setting

DOI10.1007/s10463-013-0404-2zbMath1281.62139OpenAlexW2158224295MaRDI QIDQ2434134

Yanyan Li

Publication date: 17 February 2014

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-013-0404-2

zbMATH Keywords

degenerate \(U\)-statisticsBox's approximationhigh-dimensional multivariate inference

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15)

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Cites Work

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• Degenerate \(U\)- and \(V\)-statistics under weak dependence: asymptotic theory and bootstrap consistency
• Limit theorems for von Mises statistics of a measure preserving transformation
• Analysis of high-dimensional repeated measures designs: the one sample case
• A test for the mean vector with fewer observations than the dimension under non-normality
• Decomposability of high-dimensional diversity measures: quasi-\(U\)-statistics, martingales and nonstandard asymptotics
• Functional limit theorems for U-statistics in the degenerate case
• Almost sure limit theorems for \(U\)-statistics
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• A two-sample test for high-dimensional data with applications to gene-set testing
• A central limit theorem for two-sample U-processes
• Statistical Methods for the Analysis of Repeated Measurements
• Approximation Theorems of Mathematical Statistics
• Large Sample Techniques for Statistics
• On U-statistics and v. mise? statistics for weakly dependent processes
• Asymptotic Statistics
• Multivariate Theory for Analyzing High Dimensional Data
• Modern Applied U‐Statistics
• A High Dimensional Two Sample Significance Test
• A Class of Statistics with Asymptotically Normal Distribution
• Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification
• Multivariate Statistics