A nonparametric approach to high-dimensional k-sample comparison problems

DOI10.1093/biomet/asaa015zbMath1451.62065OpenAlexW3034906196MaRDI QIDQ5127205

Subhadeep Mukhopadhyay, Kaijun Wang

Publication date: 21 October 2020

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/asaa015

zbMATH Keywords

spectral graph partitioningdistribution-free methodgraph-based nonparametric approachhigh-dimensional \(k\)-sample comparisonhigh-dimensional exploratory analysis

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Probabilistic graphical models (62H22)

Related Items (9)

Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach ⋮ Some clustering-based exact distribution-free \(k\)-sample tests applicable to high dimension, low sample size data ⋮ A Bayesian nonparametric multi-sample test in any dimension ⋮ Exhaustive Goodness of Fit Via Smoothed Inference and Graphics ⋮ A Bayesian Nonparametric Test for Cross-Group Differences Relative to a Control ⋮ A new flexible Bayesian hypothesis test for multivariate data ⋮ Asymptotic normality of Gini correlation in high dimension with applications to the \(K\)-sample problem ⋮ Comparing a large number of multivariate distributions ⋮ Informative goodness-of-fit for multivariate distributions

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