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

preprint2012arXivOpen access

Abstract

Let Xp=(s1,...,sn)=(Xij)p×n\mathbf{X}_p=(\mathbf{s}_1,...,\mathbf{s}_n)=(X_{ij})_{p \times n} where XijX_{ij}'s are independent and identically distributed (i.i.d.) random variables with EX11=0,EX112=1EX_{11}=0,EX_{11}^2=1 and EX114<∞EX_{11}^4<\infty. 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) tends to 1 almost surely as p→∞,n→∞p\rightarrow\infty,n\rightarrow\infty with p/n→0p/n\rightarrow0.

Explore this paper’s authors, topics and related work.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Reviews 0

Write a reviewWrite

No reviews yet.

Discussion 0

Add a commentWrite

No comments yet.