The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A useful variant of the Davis–Kahan theorem for statisticians
Open accessAbstract
The Davis–Kahan theorem is used in the analysis of many statistical procedures to bound the distance between subspaces spanned by population eigenvectors and their sample versions. It relies on an eigenvalue separation condition between certain population and sample eigenvalues. We present a variant of this result that depends only on a population eigenvalue separation condition, making it more natural and convenient for direct application in statistical contexts, and provide an improvement in many cases to the usual bound in the statistical literature. We also give an extension to situations where the matrices under study may be asymmetric or even non-square, and where interest is in the distance between subspaces spanned by corresponding singular vectors.
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Cites
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Subspace Estimation from Unbalanced and Incomplete Data Matrices: 𝓁2, ∞ Statistical Guarantees
Cai, C., Li, G., Chi, Y., Poor, H. V., & Chen, Y. (2021) · The Annals of Statistics · doi:10.1214/20-aos1986 · Cited by 37
CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
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Metadata
- Authors
- Yu, Y., Wang, T., & Samworth, R. J.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-04-28
- Volume
- 102
- Issue
- 2
- Pages
- 315-323
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2088911135
- Cited by
- 441
- References
- 24
- FWCI
- 20.73
- Type
- journal article
- Language
- en
- Open access
- green
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