The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Hanson-Wright inequality and sub-gaussian concentration
Open accessAbstract
In this expository note, we give a modern proof of Hanson-Wright inequality for quadratic forms in sub-gaussian random variables.We deduce a useful concentration inequality for sub-gaussian random vectors.Two examples are given to illustrate these results: a concentration of distances between random vectors and subspaces, and a bound on the norms of products of random and deterministic matrices.
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
A Mathematical Introduction to Compressive Sensing
Foucart, S., & Rauhut, H. (2013) · Applied and Numerical Harmonic Analysis · doi:10.1007/978-0-8176-4948-7 · Cited by 2,548
Introduction to the non-asymptotic analysis of random matrices
Vershynin, R. (2012) · Compressed Sensing · doi:10.1017/cbo9780511794308.006 · Cited by 2,150
Self-citationThe Concentration of Measure Phenomenon
Ledoux, M. (2005) · Mathematical Surveys and Monographs · doi:10.1090/surv/089 · Cited by 1,700
Topics in Random Matrix Theory
Tao, T. (2012) · Graduate Studies in Mathematics · doi:10.1090/gsm/132 · Cited by 868
Concentration of measure and isoperimetric inequalities in product spaces
Talagrand, M. (1995) · Publications Mathématiques de l'IHÉS · doi:10.1007/bf02699376 · Cited by 829
A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables
Hanson, D. L., & Wright, F. T. (1971) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177693335 · Cited by 326
Bulk universality for generalized Wigner matrices
Erdős, L., Yau, H.-T., & Yin, J. (2012) · Probability Theory and Related Fields · doi:10.1007/s00440-011-0390-3 · Cited by 254
Concentration inequalities for non-Lipschitz functions with bounded derivatives of higher order
Adamczak, R., & Wolff, P. (2014) · Probability Theory and Related Fields · doi:10.1007/s00440-014-0579-3 · Cited by 94
Bounded Independence Fools Degree-2 Threshold Functions
Diakonikolas, I., Kane, D. M., & Nelson, J. (2010) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2010.8 · Cited by 86
Cited by
An Efficient Framework for Clustered Federated Learning
Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3192506 · Cited by 623
Regularized estimation in sparse high-dimensional time series models
Basu, S., & Michailidis, G. (2015) · The Annals of Statistics · doi:10.1214/15-aos1315 · Cited by 345
Uncertainty quantification in the Bradley–Terry–Luce model
Gao, C., Shen, Y., & Zhang, A. Y. (2023) · Information and Inference: A Journal of the IMA · doi:10.1093/imaiai/iaac032 · Cited by 18
Sparse principal component analysis for high-dimensional stationary time series
Fujimori, K., Goto, Y., Liu, Y., & Taniguchi, M. (2023) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12664 · Cited by 7
Metadata
- Authors
- Rudelson, M., & Vershynin, R.
- Venue
- Electronic Communications in Probability
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-01-01
- Volume
- 18
- Issue
- none
- Pages
- 1-9
- ISSN
- 1083-589X (ISSN-L)
- OpenAlex
- W2032291279
- Cited by
- 502
- References
- 9
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- gold
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: Hanson-Wright inequality and sub-gaussian concentration. https://app.cochat.ai/papers/10.1214/ecp.v18-2865
- The paper
- Rudelson, M., & Vershynin, R. (2013). Hanson-Wright inequality and sub-gaussian concentration. Electronic Communications in Probability. https://doi.org/10.1214/ecp.v18-2865
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.
