Open access
Datum
2022-06Typ
- Journal Article
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000530959Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Computational Statistics & Data AnalysisBand
Seiten / Artikelnummer
Verlag
ElsevierThema
Random forest; Distribution testing; Classification; Kernel two-sample test; MMD; Total variation distance; U-statistics