Some tests of independence based on maximum mean discrepancy and ranks of nearest neighbors

DOI10.1016/j.spl.2020.108793zbMath1456.62088OpenAlexW3025963320MaRDI QIDQ2006742

Angshuman Roy, Anil Kumar Ghosh

Publication date: 12 October 2020

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2020.108793

zbMATH Keywords

Gaussian kernelnearest neighborspermutation testmaximum mean discrepancy

Mathematics Subject Classification ID

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

Related Items (1)

Статистические критерии равномерности распределения и независимости векторов, использующие попарные расстояния;Statistical tests for uniformity of distribution and independence of vectors based on pairwise distances

Uses Software

• IndependenceTests

Cites Work

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• Measuring and testing dependence by correlation of distances
• On some exact distribution-free tests of independence between two random vectors of arbitrary dimensions
• A class of multivariate distribution-free tests of independence based on graphs
• Sign test of independence between two random vectors.
• Multivariate nonparametric test of independence
• Nonparametric functional data analysis. Theory and practice.
• A Nonparametric Test of Independence Between Two Vectors
• Kernel-Based Tests for Joint Independence
• Distance Metrics for Measuring Joint Dependence with Application to Causal Inference
• A consistent multivariate test of association based on ranks of distances
• Multivariate Nonparametric Tests of Independence

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