Some strong limit theorems for the largest entries of sample correlation matrices

Abstract

Let $\{X_{k,i};i\geq 1,k\geq 1\}$ be an array of i.i.d. random variables and let $\{p_n;n\geq 1\}$ be a sequence of positive integers such that $n/p_n$ is bounded away from 0 and $\infty$. For $W_n=\max_{1\leq i<j\leq p_n}|\sum_{k=1}^nX_{k,i}X_{k,j}|$ and $L_n=\max_{1\leq i<j\leq p_n}|\hatρ^{(n)}_{i,j}|$ where $\hatρ^{(n)}_{i,j}$ denotes the Pearson correlation coefficient between $(X_{1,i},...,X_{n,i})&#39;$ and $(X_{1,j},...,X_{n,j})&#39;$, the limit laws (i) $\lim_{n\to \infty}\frac{W_n}{n^α}=0$ a.s. $(α>1/2)$, (ii) $\lim_{n\to \infty}n^{1-α}L_n=0$ a.s. $(1/2<α\leq 1)$, (iii) $\lim_{n\to \infty}\frac{W_n}{\sqrt{n\log n}}=2$ a.s. and (iv) $\lim_{n\to \infty}(\frac{n}{\log n})^{1/2}L_n=2$ a.s. are shown to hold under optimal sets of conditions. These results follow from some general theorems proved for arrays of i.i.d. two-dimensional random vectors. The converses of the limit laws (i) and (iii) are also established. The current work was inspired by Jiang&#39;s study of the asymptotic behavior of the largest entries of sample correlation matrices.