Testing the Equality of Multivariate Distributions Using the Bootstrap and Integrated Empirical Processes

DOI10.1080/03610920500498816zbMath1093.62059OpenAlexW2111734744MaRDI QIDQ5484659

Ping Jing, Jinfang Wang

Publication date: 21 August 2006

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920500498816

zbMATH Keywords

bootstrapprojection pursuitintegrated empirical processnumber-theoretic methodsintegrated empirical distribution function

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15) Nonparametric statistical resampling methods (62G09)

Related Items (8)

New two-sample tests based on the integrated empirical copula processes ⋮ Local efficiency of integrated goodness-of-fit tests under skew alternatives ⋮ Graph-theoretic multisample tests of equality in distribution for high dimensional data ⋮ Asymptotic behavior of weighted multivariate Cramér-von Mises-type statistics under contiguous alternatives ⋮ \(K\)-sample problem using strong approximations of empirical copula processes ⋮ Some applications of the strong approximation of the integrated empirical copula processes ⋮ Strong approximations for the \(p\)-fold integrated empirical process with applications to statistical tests ⋮ Some asymptotic results for the integrated empirical process with applications to statistical tests

Cites Work

• Unnamed Item
• Some limit theorems for empirical processes (with discussion)
• Bootstrapping general empirical measures
• Projection pursuit
• Confidence sets for a multivariate distribution
• A multivariate two-sample test based on the number of nearest neighbor type coincidences
• Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
• Bootstrap methods: another look at the jackknife
• On the multivariate runs test
• Two-Sample Tests Based on the Integrated Empirical Process
• Convergence of stochastic processes

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