ArticleAnnals of statistics2021
ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING.
Article in Annals of statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data.Biometrics · 2025Article
- A Novel Approach of High Dimensional Linear Hypothesis Testing Problem.Journal of the American Statistical Association · 2025Article
- A unified combination framework for dependent tests with applications to microbiome association studies.Biometrics · 2025Article
- Use of random integration to test equality of high dimensional covariance matrices.Statistica Sinica · 2023Article
- Finite sample t-tests for high-dimensional means.Journal of multivariate analysis · 2023Article
- Linear Hypothesis Testing in Linear Models With High-Dimensional Responses.Journal of the American Statistical Association · 2022Article
- ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING.Annals of statistics · 2021Article
- A powerful fine-mapping method for transcriptome-wide association studies.Human genetics · 2020Article
- A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.Journal of machine learning research : JMLR · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Many high-dimensional hypothesis tests aim to globally examine marginal or low-dimensional features of a high-dimensional joint distribution, such as testing of mean vectors, covariance matrices and regression coefficients. This paper constructs a family of U-statistics as unbiased estimators of the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.