# Trace of the inverse sample covariance as the number of samples and dimension scale to infinity

Let $x_1,\dots,x_n$ be i.i.d. $N(0,I_{p\times p})$, with $n>p$. Let $\hat S=\frac1n\sum_{i=1}^n x_i x_i^T$ be the sample covariance.

Assume the asymptotic setting where $\frac pn\to \alpha<1$.

Is there a result about the concentration of $\mathbb{E}\left[\operatorname{tr}\left(\hat{S}^{-1}\right)\right]$ in the asymptotic setting? By using Lemma 3.2 in this paper coupled with the Sherman-Morrison formula I believe that $\mathbb{E}\left[\operatorname{tr}\left(\hat{S}^{-1}\right)\right]=\frac{1}{1-\alpha}$, but I haven't been able to make it rigorous.

Writing $\hat{S}= \frac1n XX^T$ where $X$ has columns $x_1,\dots,x_n$, we can express $$\text{tr}(\hat{S}^{-1}) = \sum_{i=1}^p \lambda_i(\hat{S})^{-1} = n\sum_{i=1}^p \sigma_i(X)^{-2}$$ where $\sigma_1(X), \dots, \sigma_p(X)$ are the nonzero singular values of $X$. Then we can use the inverse second moment identity (observed by Tao and Vu here http://arxiv.org/pdf/0807.4898v5.pdf; the rectangular case is given as Lemma 4.14 here: https://arxiv.org/pdf/1109.3343v4.pdf) to express $$\sum_{i=1}^p \sigma_i(X)^{-2}=\sum_{i=1}^p \text{dist}(R_i,R_{-i})^{-2}$$ where $\text{dist}(R_i,R_{-i})$ is the distance from the $i$th row of $X$ to the span of the remaining rows. Since $X$ has normally distributed entries the variables $\text{dist}(R_i,R_{-i})^{2}$ have chi-squared distribution, so the expectation of this sum can be computed explicitly. Unless I have made some mistake it comes out to $\mathbb{E}\frac1p\text{tr}(\hat{S}^{-1}) = \frac{n}{n-p-1} = \frac{1}{1-\alpha}+o(1)$ (note we need the normalization $1/p$).