NonparametricK-Sample Tests via Dynamic Slicing

DOI10.1080/01621459.2014.920257zbMath1373.62195OpenAlexW1967133718MaRDI QIDQ5367388

Bo Jiang, Chao Ye, Jun S. Liu

Publication date: 13 October 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2014.920257

zbMATH Keywords

dynamic programmingregularizationmutual informationKullback-Leibler divergencetwo-sample testKolmogorov-Smirnov testnonparametric \(K\)-sample test

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Dynamic programming (90C39)

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Dimension reduction via adaptive slicing ⋮ Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach ⋮ An empirical study of the maximal and total information coefficients and leading measures of dependence ⋮ Fused mean-variance filter for feature screening ⋮ High-dimensional general linear hypothesis testing under heteroscedasticity ⋮ A distribution-free test of independence based on a modified mean variance index ⋮ Asymptotic normality of Gini correlation in high dimension with applications to the \(K\)-sample problem ⋮ Comparing a large number of multivariate distributions ⋮ Equitability, interval estimation, and statistical power ⋮ Analysis of Distributional Variation Through Graphical Multi-Scale Beta-Binomial Models

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