Publication: On the use of random forest for two-sample testing
On the use of random forest for two-sample testing
Date
Date
Date
Citations
Hediger, S., Michel, L., & Näf, J. (2022). On the use of random forest for two-sample testing. Computational Statistics & Data Analysis, 170, 107435. https://doi.org/10.1016/j.csda.2022.107435
Abstract
Abstract
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 R^d. 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
Metrics
Downloads
Views
Additional indexing
Creators (Authors)
- Hediger, Simon
- Michel, Loris
- Näf, Jeffrey
Journal/Series Title
Journal/Series Title
Journal/Series Title
Volume
Volume
Volume
Page range/Item number
Page range/Item number
Page range/Item number
Item Type
Item Type
Item Type
Scope
Scope
Scope
Language
Language
Language
Publication date
Publication date
Publication date
Date available
Date available
Date available
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
OA Status
OA Status
OA Status
Free Access at
Free Access at
Free Access at
Publisher DOI
Other Identification Number
Other Identification Number
Other Identification Number
Metrics
Downloads
Views
Citations
Hediger, S., Michel, L., & Näf, J. (2022). On the use of random forest for two-sample testing. Computational Statistics & Data Analysis, 170, 107435. https://doi.org/10.1016/j.csda.2022.107435
