Spectral measure of large random Hankel, Markov and Toeplitz matrices

dc.creatorBryc, Włodzimierz
dc.creatorDembo, Amir
dc.creatorJiang, Tiefeng
dc.date2003-07-25
dc.date2006-02-27
dc.date.accessioned2026-07-07T08:06:09Z
dc.date.available2026-07-07T08:06:09Z
dc.descriptionWe study the limiting spectral measure of large symmetric random matrices of linear algebraic structure. For Hankel and Toeplitz matrices generated by i.i.d. random variables $\{X_k\}$ of unit variance, and for symmetric Markov matrices generated by i.i.d. random variables $\{X_{ij}\}_{j>i}$ of zero mean and unit variance, scaling the eigenvalues by $\sqrt{n}$ we prove the almost sure, weak convergence of the spectral measures to universal, nonrandom, symmetric distributions $γ_H$, $γ_M$ and $γ_T$ of unbounded support. The moments of $γ_H$ and $γ_T$ are the sum of volumes of solids related to Eulerian numbers, whereas $γ_M$ has a bounded smooth density given by the free convolution of the semicircle and normal densities. For symmetric Markov matrices generated by i.i.d. random variables $\{X_{ij}\}_{j>i}$ of mean $m$ and finite variance, scaling the eigenvalues by ${n}$ we prove the almost sure, weak convergence of the spectral measures to the atomic measure at $-m$. If $m=0$, and the fourth moment is finite, we prove that the spectral norm of $\mathbf {M}_n$ scaled by $\sqrt{2n\log n}$ converges almost surely to 1.
dc.descriptionPublished at http://dx.doi.org/10.1214/009117905000000495 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0307330
dc.identifierhttp://arxiv.org/abs/math/0307330
dc.identifierAnnals of Probability 2006, Vol. 34, No. 1, 1-38
dc.identifierdoi:10.1214/009117905000000495
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130511
dc.subjectProbability
dc.subjectCombinatorics
dc.subjectStatistics Theory
dc.subject15A52 (Primary) 60F99, 62H10, 60F10 (Secondary)
dc.titleSpectral measure of large random Hankel, Markov and Toeplitz matrices
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