Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero

Let Xp = (s1, . . . , sn) = (Xij )p×n where Xij ’s are independent and identically distributed (i.i.d.) random variables with EX11 = 0, EX2 11 = 1 and EX4 11 <1. It is showed that the largest eigen- value of the random matrix Ap = 1 2√np (XpX′p −nIp) tends to 1 almost surely as p→∞,n→∞ with p/n→0...

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Main Authors: Chen, B. B., Pan, G. M.
Other Authors: School of Physical and Mathematical Sciences
Format: Journal Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98864
http://hdl.handle.net/10220/12678
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author Chen, B. B.
Pan, G. M.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Chen, B. B.
Pan, G. M.
author_sort Chen, B. B.
collection NTU
description Let Xp = (s1, . . . , sn) = (Xij )p×n where Xij ’s are independent and identically distributed (i.i.d.) random variables with EX11 = 0, EX2 11 = 1 and EX4 11 <1. It is showed that the largest eigen- value of the random matrix Ap = 1 2√np (XpX′p −nIp) tends to 1 almost surely as p→∞,n→∞ with p/n→0.
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spelling ntu-10356/988642023-02-28T19:22:56Z Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero Chen, B. B. Pan, G. M. School of Physical and Mathematical Sciences Let Xp = (s1, . . . , sn) = (Xij )p×n where Xij ’s are independent and identically distributed (i.i.d.) random variables with EX11 = 0, EX2 11 = 1 and EX4 11 <1. It is showed that the largest eigen- value of the random matrix Ap = 1 2√np (XpX′p −nIp) tends to 1 almost surely as p→∞,n→∞ with p/n→0. Published Version 2013-08-01T01:06:17Z 2019-12-06T20:00:36Z 2013-08-01T01:06:17Z 2019-12-06T20:00:36Z 2012 2012 Journal Article Chen, B. B., & Pan, G. M. (2012). Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero. Bernoulli, 18(4), 1405-1420. 1350-7265 https://hdl.handle.net/10356/98864 http://hdl.handle.net/10220/12678 10.3150/11-BEJ381 en Bernoulli © 2012 ISI/BS. This paper was published in Bernoulli and is made available as an electronic reprint (preprint) with permission of Bernoulli Society for Mathematical Statistics and Probability. The paper can be found at the following official DOI: [http://dx.doi.org/10.3150/11-BEJ381]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf
spellingShingle Chen, B. B.
Pan, G. M.
Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title_full Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title_fullStr Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title_full_unstemmed Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title_short Convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
title_sort convergence of the largest eigenvalue of normalized sample covariance matrices when p and n both tend to infinity with their ratio converging to zero
url https://hdl.handle.net/10356/98864
http://hdl.handle.net/10220/12678
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