Is there a bound on the norm of the product of second moment matrix with random vector? Let $X_1,\dots,X_n$ be vectors in $\mathbb{R^d}$. Assume all of the vectors are inside the unite $\ell_2$ ball, but outside the ball of radius $r$ for some $r \in (0,1)$, i.e. $r \leq \|X_i\| \leq 1$ . Let $P$ be a vector in the probability simplex $\Delta_n$ with $P_i>0$ for all $i$. Consider the second moment matrix $\Sigma(P) = \sum_{i=1}^n P_i X_i X_i^\top$. Assume the $X_i$s are such that $\Sigma(P)$ is full rank. Does the following bound always hold? If not, when does it hold?
$$\|\Sigma(P)^{-1}X_j\| \leq \frac{1}{r P_j} \quad \forall j\in \{1,\dots,n\}$$
For instance, if $n=d$ and $X_i=e_i$ are the canonical basis vectors of $\mathbb{R}^d$, then this bounds holds with equality.
 A: $\newcommand\Si{\Sigma}$
$\newcommand\X{\mathbf X}$
The answer is no. Indeed, let $p_i:=P_i$, $p:=P$, $\X:=(X_1,\dots,X_n)$, and $\Si_\X:=\Si(p)$. At least one of the vectors $\Si^{-1}X_j$ is nonzero, for some $j$, because otherwise the matrix
$$I=\Si_\X^{-1}\Si_\X=\sum_1^n p_i\Si_\X^{-1}X_i X_i^T$$
would be zero. So, there is some $j$ such that $c:=\|\Si_\X^{-1}X_j\|>0$. Replacing now $\X$ by $a\X$ for real $a>0$ and letting $a\to0$, we will have 
$$\|\Si_{a\X}^{-1}(aX_j)\|=\frac1a\,\|\Si_\X^{-1} X_j\|=\frac ca\to\infty,$$
so that the inequality 
$$\|\Si_{a\X}^{-1}(aX_j)\|\le\frac1{p_j}$$
will fail to hold for small enough $a$. 

The OP has edited the question, thus invalidating this answer. However, even after the edit, the answer remains no. E.g., let $n=2$, $p_1=p_2=1/2$, $X_1:=[1,1]^T/\sqrt2$, and $X_2:=[1,1-h]^T/\sqrt2$ with $h\downarrow0$. Then $\|X_1\|=1$, $1\ge\|X_2\|\sim1$, but 
$$\|\Si_{\X}^{-1}(X_1)\|=\frac{2 \sqrt{2} \sqrt{h^2-2 h+2}}{h}
\sim\frac4h
\not\lesssim2=\frac1{p_1}.$$
