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Some limit theorems on the eigenvectors of large dimensional sample covariance matrices

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  • Silverstein, Jack W.
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    Abstract

    Let {vij} i,j = 1, 2,..., be i.i.d. standardized random variables. For each n, let Vn = (vij) I = 1, 2,..., n; J = 1, 2,..., S = s(n), where (n/s) --> y > 0 as n --> [infinity], and let Mn = (1/s)VnVnT. Previous results [7, 8] have shown the eigenvectors of Mn to display behavior, for n large, similar to those of the corresponding Wishart matrix. A certain stochastic process Xn on [0, 1], constructed from the eigenvectors of Mn, is known to converge weakly, as n --> [infinity], on D[0, 1] to Brownian bridge when v11 is N(0, 1), but it is not known whether this property holds for any other distribution. The present paper provides evidence that this property may hold in the non-Wishart case in the form of limit theorems on the convergence in distribution of random variables constructed from integrating analytic function w.r.t. Xn(Fn(x)), where Fn is the empirical distribution function of the eigenvalues of Mn. The theorems assume certain conditions on the moments of v11 including E(v114) = 3, the latter being necessary for the theorems to hold.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 15 (1984)
    Issue (Month): 3 (December)
    Pages: 295-324

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    Handle: RePEc:eee:jmvana:v:15:y:1984:i:3:p:295-324

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    Related research

    Keywords: Sample covariance matrices behavior of eigenvectors Brownian bridge weak convergence multidimensional method of moments;

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    Cited by:
    1. Olivier Ledoit & Sandrine Péché, 2009. "Eigenvectors of some large sample covariance matrices ensembles," IEW - Working Papers 407, Institute for Empirical Research in Economics - University of Zurich.

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