The Annals of Probability

Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices

Jinho Baik, Gérard Ben Arous, and Sandrine Péché

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We compute the limiting distributions of the largest eigenvalue of a complex Gaussian sample covariance matrix when both the number of samples and the number of variables in each sample become large. When all but finitely many, say r, eigenvalues of the covariance matrix are the same, the dependence of the limiting distribution of the largest eigenvalue of the sample covariance matrix on those distinguished r eigenvalues of the covariance matrix is completely characterized in terms of an infinite sequence of new distribution functions that generalize the Tracy–Widom distributions of the random matrix theory. Especially a phase transition phenomenon is observed. Our results also apply to a last passage percolation model and a queueing model.

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Ann. Probab. Volume 33, Number 5 (2005), 1643-1697.

First available in Project Euclid: 22 September 2005

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Primary: 15A52 41A60: Asymptotic approximations, asymptotic expansions (steepest descent, etc.) [See also 30E15] 60F99: None of the above, but in this section 62E20: Asymptotic distribution theory 62H20: Measures of association (correlation, canonical correlation, etc.)

Sample covariance limit theorem Tracy–Widom distribution Airy kernel random matrix


Baik, Jinho; Ben Arous, Gérard; Péché, Sandrine. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices. Ann. Probab. 33 (2005), no. 5, 1643--1697. doi:10.1214/009117905000000233.

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