{ "id": "1010.3394", "version": "v2", "published": "2010-10-17T04:04:29.000Z", "updated": "2010-11-08T18:13:13.000Z", "title": "Fluctuation of Eigenvalues for Random Toeplitz and Related Matrices", "authors": [ "Dang-Zheng Liu", "Xin Sun", "Zheng-Dong Wang" ], "comment": "27 pages, corrected small gap in proof of Theorem 1.1, added remark 1.3", "categories": [ "math.PR" ], "abstract": "Consider random symmetric Toeplitz matrices $T_{n}=(a_{i-j})_{i,j=1}^{n}$ with matrix entries $a_{j}, j=0,1,2,...,$ being independent real random variables such that \\be \\mathbb{E}[a_{j}]=0, \\ \\ \\mathbb{E}[|a_{j}|^{2}]=1 \\ \\ \\textrm{for}\\,\\ \\ j=0,1,2,...,\\ee (homogeneity of 4-th moments) \\be{\\kappa=\\mathbb{E}[|a_{j}|^{4}],}\\ee \\noindent and further (uniform boundedness)\\be\\sup\\limits_{j\\geq 0} \\mathbb{E}[|a_{j}|^{k}]=C_{k}<\\iy\\ \\ \\ \\textrm{for} \\ \\ \\ k\\geq 3.\\ee Under the assumption of $a_{0}\\equiv 0$, we prove a central limit theorem for linear statistics of eigenvalues for a fixed polynomial with degree $\\geq 2$. Without the assumption, the CLT can be easily modified to a possibly non-normal limit law. In a special case where $a_{j}$'s are Gaussian, the result has been obtained by Chatterjee for some test functions. Our derivation is based on a simple trace formula for Toeplitz matrices and fine combinatorial analysis. Our method can apply to other related random matrix models, including Hankel matrices and product of several Toeplitz matrices in a flavor of free probability theory etc. Since Toeplitz matrices are quite different from the Wigner and Wishart matrices, our results enrich this topic.", "revisions": [ { "version": "v2", "updated": "2010-11-08T18:13:13.000Z" } ], "analyses": { "subjects": [ "60F05", "60B20" ], "keywords": [ "random toeplitz", "related matrices", "eigenvalues", "fluctuation", "random symmetric toeplitz matrices" ], "note": { "typesetting": "TeX", "pages": 27, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2010arXiv1010.3394L" } } }