Limiting spectral distribution of $X{X}^{\text{'}}$ matrices

Bose, Arup; Gangopadhyay, Sreela; Sen, Arnab

Annales de l'I.H.P. Probabilités et statistiques, Tome 46 (2010) no. 3, pp. 677-707.

Une des méthodes pour obtenir la limite des distributions spectrales (LSD) des grandes matrices aléatoires est la fameuse méthode des moments, basée sur la formule des traces. Son succès a été clairement établi pour différents types de matrices telles que les matrices de Wigner et les matrices de covariance. Dans un article récent, Bryc, Dembo et Jiang [Ann. Probab. 34 (2006) 1-38] ont obtenu la LSD pour des matrices de Toeplitz et de Hankel en utilisant cette méthode. Ils arrivent à estimer les traces des moments de telles matrices en séparant les différents termes par classes d'équivalence et en reliant les asymptotiques des dénombrements afférents avec les calculs de certains volumes. Bose et Sen [Electron. J. Probab. 13 (2008) 588-628] ont développé cette idée et ont donné un cadre général pour traiter de matrices symmétriques dont les entrées viennent d'une suite indépendante. Dans cet article, nous généralisons cette approche pour considérer des matrices de la form où X est une matrice p×n avec des entrées réelles. Nous démontrons un résultat général d'existence de la LSD de telles matrices, correctement recentrées et rééchelonnées, quand p et n tendent vers l'infini de telle façon que p/n tende vers y∈(0, ∞). Par exemple, nous montrons l'existence de la LSD quand X est la matrice asymétrique de Hankel, de Toeplitz, circulante ou circulante inverse. En particulier, quand y=0, les limites correspondent à celles obtenues par Bryc, Dembo et Jiang [Ann. Probab. 34 (2006) 1-38]. Sinon, nous obtenons de nouvelles lois limites pour lesquelles aucune expression explicite n'est connue. Nous étudions ces lois par quelques simulations.

The methods to establish the limiting spectral distribution (LSD) of large dimensional random matrices includes the well-known moment method which invokes the trace formula. Its success has been demonstrated in several types of matrices such as the Wigner matrix and the sample covariance matrix. In a recent article Bryc, Dembo and Jiang [Ann. Probab. 34 (2006) 1-38] establish the LSD for random Toeplitz and Hankel matrices using the moment method. They perform the necessary counting of terms in the trace by splitting the relevant sets into equivalence classes and relating the limits of the counts to certain volume calculations. Bose and Sen [Electron. J. Probab. 13 (2008) 588-628] have developed this method further and have provided a general framework which deals with symmetric matrices with entries coming from an independent sequence. In this article we enlarge the scope of the above approach to consider matrices of the form where X is a p×n matrix with real entries. We establish some general results on the existence of the spectral distribution of such matrices, appropriately centered and scaled, when p→∞ and n=n(p)→∞ and p/n→y with 0≤y<∞. As examples we show the existence of the spectral distribution when X is taken to be the appropriate asymmetric Hankel, Toeplitz, circulant and reverse circulant matrices. In particular, when y=0, the limits for all these matrices coincide and is the same as the limit for the symmetric Toeplitz derived in Bryc, Dembo and Jiang [Ann. Probab. 34 (2006) 1-38]. In other cases, we obtain new limiting spectral distributions for which no closed form expressions are known. We demonstrate the nature of these limits through some simulation results.

Classification : 60F05, 60F15, 62E20, 60G57
Mots clés : large dimensional random matrix, eigenvalues, sample covariance matrix, Toeplitz matrix, Hankel matrix, circulant matrix, reverse circulant matrix, spectral distribution, bounded Lipschitz metric, limiting spectral distribution, moment method, volume method, almost sure convergence, convergence in distribution

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