The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A two-sample test for high-dimensional data with applications to gene-set testing
Open accessAbstract
We propose a two-sample test for the means of high-dimensional data when the data dimension is much larger than the sample size. Hotelling’s classical T2 test does not work for this “large p, small n” situation. The proposed test does not require explicit conditions in the relationship between the data dimension and sample size. This offers much flexibility in analyzing high-dimensional data. An application of the proposed test is in testing significance for sets of genes which we demonstrate in an empirical study on a leukemia data set.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Cited by
Tests for High-Dimensional Covariance Matrices
Chen, S. X., Zhang, L.-X., & Zhong, P.-S. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2010.tm09560 · Cited by 329
Self-citationCondran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022) · IEEE Access · doi:10.1109/access.2022.3188649 · Cited by 99
Identifying cointegration by eigenanalysis
Zhang, R., Robinson, P., & Yao, Q. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1458620 · Cited by 51
On kernel methods for covariates that are rankings
Mania, H., Ramdas, A., Wainwright, M. J., Jordan, M. I., & Recht, B. (2018) · Electronic Journal of Statistics · doi:10.1214/18-ejs1437 · Cited by 18
Metadata
- Authors
- Chen, S. X., & Qin, Y.-L.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-04-01
- Volume
- 38
- Issue
- 2
- Pages
- 808-835
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2081874429
- Cited by
- 574
- References
- 2
- FWCI
- 15.35
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A two-sample test for high-dimensional data with applications to gene-set testing. https://app.cochat.ai/papers/10.1214/09-aos716
- The paper
- Chen, S. X., & Qin, Y.-L. (2010). A two-sample test for high-dimensional data with applications to gene-set testing. The Annals of Statistics. https://doi.org/10.1214/09-aos716
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
