Papers › Scaling property of the statistical Two-Sample Energy Test

Scaling property of the statistical Two-Sample Energy Test

27 Apr 2018arXiv:1804.10599links table onlyarchive 2025-07-28

G. Zech

The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.

The energy test is a powerful binning-free, multi-dimensional and distribution-free tool that can be applied to compare a measurement to a given prediction (goodness-of-fit) or to check whether two data samples originate from the same population (two-sample test). In both cases the distribution of the test statistic under the null hypothesis H_0, (correct prediction, same population) has to be obtained by simulation. This poses computational problems if the data samples are large, but the difficulty can be overcome with the help of a scaling property which relates the distribution of small samples to the distribution of large samples. Scaling has been made plausible in Ref. W. Barter et al. JINST 13 P04011 by extensive simulations. In this article an analytic proof is presented which makes the calculation of p-values obtained by scaling more reliable.

PaperPDFCode

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections