On the use of random forest for two-sample testing

DOI10.1016/j.csda.2022.107435OpenAlexW4210644179MaRDI QIDQ2129579

Jeffrey Näf, Loris Michel, Simon Hediger

Publication date: 22 April 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.06287

zbMATH Keywords

classificationMMDU-statisticsrandom foresttotal variation distancedistribution testingkernel two-sample test

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Renewal type bootstrap for increasing degree \(U\)-process of a Markov chain ⋮ Global and local two-sample tests via regression ⋮ hypoRF

Uses Software

• R
• QRM
• datamicroarray

Cites Work

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• Permutation tests. A practical guide to resampling methods for testing hypotheses
• A computationally fast variable importance test for random forests for high-dimensional data
• Exact testing with random permutations
• The classification permutation test: a flexible approach to testing for covariate imbalance in observational studies
• Global and local two-sample tests via regression
• A random forest guided tour
• Classification accuracy as a proxy for two-sample testing
• The t Copula and Related Copulas
• Asymptotic Statistics
• Two‐sample test based on classification probability
• Random forests

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