Let $X: \Omega \to \mathbb{R}^{p \times n}$ be a random matrix so that each entry $X_{ij}$ is a random variable with $\mathbb{E}X_{ij}=0, \mathbb{E}X_{ij}^2=\sigma^2$

I was wondering what would happen if we keep every hypothesis in the Marcenko-Pastur theorem, *but change only one*: assume that $p/n \to 0$ or $p/n \to \infty$. Below I'll refer time and again to the above link and the symbols used therein.

From the numerical experiements that I'm doing and from the expression of $\lambda_{+, -}$ in the link above, it looks like:

(1) **When $p/n \to 0$,** the empirical spectral ditribution of $Cov := \frac{1}{n}XX^{*}$ approaches the Dirac measure at $\sigma^2$. This is apparent from the numerical experiments, and also from the fact that in this case, when $\lambda:= lim_{p,n\to \infty}p/n < 1$, there's no isolated mass, but the continuous part defined using $\nu$ has support $\lambda_{+, -}$, and as $\lambda \to 0$, this support shrinks to $\sigma^2$.

(2) **When $p/n \to \infty$,** he empirical spectral ditribution of $Cov := \frac{1}{n}XX^{*}$ approaches the Dirac measure at $0$. This is because when $\lambda:= lim_{p,n\to \infty}p/n > 1$, then the Marcenko Patur distribution has two parts: one isolated mass at $0$ with weight $1-\frac{1}{\lambda}$, and the other is the contonuous distribution with density $\nu$, and with support $[\sigma^2(1-\frac{1}{\lambda})^2, \sigma^2(1+\frac{1}{\lambda})^2]$. Clearly the first isolated pass approach the Dirac measure and the second part approaches $0$ as its support moves infinitely away.

But the above two explanations are heuristics. Is there a mathematically rigorous proof where they actually prove these statements, say perhaps using the Stieltjes' transform method, so proving that for example when $p/n \to \infty$, the Stieltjes' transform ($ST$) of $ESD(cov X)\to -\frac{1}{z}= ST(\delta_0)$

**N.B.** *I tried to look this up on the internet, but was a bit surprised by the lack of resources available. I wonder why there isn't much on these extreme cases?*