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

23 November 2012
B. Chen
G. Pan
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Abstract

Let Xp=(s1,...,sn)=(Xij)p×n\mathbf{X}_p=(\mathbf{s}_1,...,\mathbf{s}_n)=(X_{ij})_{p \times n}Xp​=(s1​,...,sn​)=(Xij​)p×n​ where XijX_{ij}Xij​'s are independent and identically distributed (i.i.d.) random variables with EX11=0,EX112=1EX_{11}=0,EX_{11}^2=1EX11​=0,EX112​=1 and EX114<∞EX_{11}^4<\inftyEX114​<∞. It is showed that the largest eigenvalue of the random matrix Ap=12np(XpXp′−nIp)\mathbf{A}_p=\frac{1}{2\sqrt{np}}(\mathbf{X}_p\mathbf{X}_p^{\prime}-n\mathbf{I}_p)Ap​=2np​1​(Xp​Xp′​−nIp​) tends to 1 almost surely as p→∞,n→∞p\rightarrow\infty,n\rightarrow\inftyp→∞,n→∞ with p/n→0p/n\rightarrow0p/n→0.

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