Quadratic form of heavy-tailed self-normalized random vector with applications in $α$-heavy Mar\v cenko--Pastur law

Abstract: Let $\mathbf{x}$ be a random vector with $n$ i.i.d.\ real-valued components in the domain attraction of an $α$-stable law with $α\in(0,2)$, and let $\mathbf{y}=\mathbf{x}/|\mathbf{x}|2$ be the associated self-normalized vector on the unit sphere. For a (possibly random) Hermitian matrix $\mathbf{A}_n=\big(a{ij}^{(n)}\big)$ independent of $\mathbf{y}$, we study the asymptotic law of the quadratic form $\mathbf{y}^\top \mathbf{A}n \mathbf{y}$. Building on the sharp separation between diagonal and off-diagonal contributions in this heavy-tailed setting, we show that under a mild assumption on the Frobenius norm of the off-diagonal part of $\mathbf{A}_n$ the limiting law is solely governed by the empirical distribution of the diagonal entries and the index $α$. More precisely, if $n^{-1}\sum{i=1}^n δ{a^{(n)}{ii}}$ converges weakly almost surely to a deterministic $ν$, then $Q_n$ converges in distribution to a non-degenerate law $μ{ν,α}$ characterized through its Stieltjes transform. The law $μ{ν,α}$ is shown to be atom-free (provided that $ν$ is non-degenerate) with an explicit density and tractable tail behavior. As an application in random matrix theory, we derive an implicit resolvent-based representation of the $α$-heavy Marčenko--Pastur law $H_{α,γ}$ for heavy-tailed sample correlation matrices and prove that $H_{α,γ}$ has no atoms except possibly at the origin. For comparison with the light-tailed setting, we also provide a Hanson--Wright-type concentration inequality for $\mathbf{y}^\top \mathbf{A}_n \mathbf{y}$ when the components of $\mathbf{x}$ are sub-Gaussian.