Papers › Two-Sample High Dimensional Mean Test Based On Prepivots
Two-Sample High Dimensional Mean Test Based On Prepivots
Santu Ghosh, Deepak Nag Ayyala, Rafael Hellebuyck
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.
Testing equality of mean vectors is a very commonly used criterion when comparing two multivariate random variables. Traditional tests such as Hotelling's T-squared become either unusable or output small power when the number of variables is greater than the combined sample size. In this paper, w}e propose a test using both prepivoting and Edgeworth expansion for testing the equality of two population mean vectors in the "large p, small n" setting. The asymptotic null distribution of the test statistic is derived and it is shown that the power of suggested test converges to one under certain alternatives when both n and p increase to infinity against sparse alternatives. Finite sample performance of the proposed test statistic is compared with other recently developed tests designed to also handle the "large p, small n" situation through simulations. The proposed test achieves competitive rates for both type I error rate and power. The usefulness of our test is illustrated by applications to two microarray gene expression data sets
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