For a n-dimensional vector $\mathbf{x}$, a $n\times n$ correlation matrix $\mathbf{R}$ is https://en.wikipedia.org/wiki/Covariance_matrix#Correlation_matrix
\begin{equation} \mathbf{R} = {E}\big[(\mathbf{x}-E(\mathbf{x}))(\mathbf{x}-E(\mathbf{x}))^T\big]\tag{1a} \end{equation}
where $E(.)$ is expectation operator. If $E(\mathbf{x})=0$, the correlation $\mathbf{R}$ reduces to
\begin{equation} \mathbf{R} = {E}\big[\mathbf{x}^{}\mathbf{x}^T\big]\tag{1b} \end{equation}
The estimate of $\mathbf{R}$, call it $\mathbf{R_{xx}}$, can be computed by collecting $N$ independent n-dimensional sample vectors $\mathbf{x}$ (http://perso-math.univ-mlv.fr/users/banach/workshop2010/talks/Vershynin.pdf)
\begin{equation} \mathbf{R_{xx}} = \frac{1}{(N-1)}\sum_{i=1}^{N} \mathbf{x}_i\mathbf{x}_i^T \tag{2} \end{equation}
My question are
- what is the $rank(\mathbf{R})$
- what is the $rank(\mathbf{R_{xx}})$ when $N>>n$
From (1b), $rank(\mathbf{R})$ should be 1. For (2), I searched for "rank of sum of rank-1 matrices" and found this post Rank of sum of rank-1 matrices which essentially says that rank of sum of rank-1 matrices as be as high as n for independent vectors. These are two conflicting things and I am not able to understand what I am missing here.