On the use of random forest for two-sample testing

Simon Hediger, Loris Michel and Jeffrey Näf

Computational Statistics & Data Analysis, 2022, vol. 170, issue C

Abstract: Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on Rd. Furthermore, the built-in variable importance measure of the Random Forest gives potential insights into which variables make out the difference in distribution. An asymptotic power analysis for the proposed tests is conducted. Finally, two real-world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, the R-package “hypoRF” is provided.

Keywords: Random forest; Distribution testing; Classification; Kernel two-sample test; MMD; Total variation distance; U-statistics (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000159

DOI: 10.1016/j.csda.2022.107435

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