Eigenvalue Estimation of Parametrized Covariance Matrices of Large Dimensional Data

Jianfeng Yao (1) , Abla Kammoun (1) , Jamal Najim (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information

This paper deals with the problem of estimating the covariance matrix of a series of independent multivariate observations, in the case where the dimension of each observation is of the same order as the number of observations. Although such a regime is of interest for many current statistical signal processing and wireless communication issues, traditional methods fail to produce consistent estimators and only recently results relying on large random matrix theory have been unveiled. In this paper, we develop the parametric framework proposed by Mestre, and consider a model where the covariance matrix to be estimated has a (known) finite number of eigenvalues, each of it with an unknown multiplicity. The main contributions of this work are essentially threefold with respect to existing results, and in particular to Mestre's work: To relax the (restrictive) separability assumption, to provide joint consistent estimates for the eigenvalues and their multiplicities, and to study the variance error by means of a Central Limit theorem.

Mots clés: Large random matrices; covariance estimation; population matrix estimation

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Théorie de l'information [cs.IT]; Théorie de l'information et codage [math.IT]; Méthodologie [stat.ME]

Jianfeng Yao, Abla Kammoun, Jamal Najim. Eigenvalue Estimation of Parametrized Covariance Matrices of Large Dimensional Data. IEEE Transactions on Signal Processing, 2012, 60 (11), pp.5893-5905. ⟨10.1109/TSP.2012.2212016⟩. ⟨hal-00662061⟩