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Large deviations of extremal eigenvalues of sample covariance matrices

Part of: Probability theory on algebraic and topological structures Basic linear algebra Limit theorems

Published online by Cambridge University Press:  24 April 2023

Denise Uwamariya  and
Xiangfeng Yang
Denise Uwamariya*
Affiliation:
Linköping University
Xiangfeng Yang*
Affiliation:
Linköping University
*
*Postal address: Department of Mathematics, Linköping University, SE-581 83 Linköping, Sweden.
*Postal address: Department of Mathematics, Linköping University, SE-581 83 Linköping, Sweden.
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Abstract

Large deviations of the largest and smallest eigenvalues of $\mathbf{X}\mathbf{X}^\top/n$ are studied in this note, where $\mathbf{X}_{p\times n}$ is a $p\times n$ random matrix with independent and identically distributed (i.i.d.) sub-Gaussian entries. The assumption imposed on the dimension size p and the sample size n is $p=p(n)\rightarrow\infty$ with $p(n)={\mathrm{o}}(n)$. This study generalizes one result obtained in [3].

Keywords

Large deviationssample covariance matricesextremal eigenvalues

MSC classification

Primary: 60B20: Random matrices (probabilistic aspects; for algebraic aspects see )
Secondary: 60F10: Large deviations 15A12: Conditioning of matrices
Type
Original Article
Information
Journal of Applied Probability , Volume 60 , Issue 4 , December 2023 , pp. 1275 - 1280
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Applied Probability Trust

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