Regularized LRT for large scale covariance matrices: One sample problem

A Clt for Regularized Sample Covariance Matrices

We consider the spectral properties of a class of regularized estimators of (large) empirical covariance matrices corresponding to stationary (but not necessarily Gaussian) sequences, obtained by banding. We prove a law of large numbers (similar to that proved in the Gaussian case by Bickel and Levina), which implies that the spectrum of a banded empirical covariance matrix is an efficient esti...

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Regularized estimation of large covariance matrices

This paper considers estimating a covariance matrix of p variables from n observations by either banding or tapering the sample covariance matrix, or estimating a banded version of the inverse of the covariance. We show that these estimates are consistent in the operator norm as long as (logp)/n→ 0, and obtain explicit rates. The results are uniform over some fairly natural well-conditioned fam...

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A Clt for Regularized Sample Covariance Matrices by Greg

We consider the spectral properties of a class of regularized estimators of (large) empirical covariance matrices corresponding to stationary (but not necessarily Gaussian) sequences, obtained by banding. We prove a law of large numbers (similar to that proved in the Gaussian case by Bickel and Levina), which implies that the spectrum of a banded empirical covariance matrix is an efficient esti...

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6 a Clt for Regularized Sample Covariance Matrices Greg

We consider the spectral properties of a class of regularized estimators of (large) empirical covariance matrices corresponding to stationary (but not necessarily Gaussian) sequences, obtained by banding. We prove a law of large numbers (similar to that proved in the Gaussian case by Bickel and Levina), which implies that the spectrum of a banded empirical covariance matrix is an efficient esti...

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On the Eigenvectors of Large Dimensional Sample Covariance Matrices

Let {a,}, i,j=1,2 ,..., be i.i.d. random variables, and for each n let M, = (l/s) V, Vz, where V, = (vi,). i = 1,2, . . . . n, j = 1,2, . . . . s = s(n), and n/s -+ y > 0 as n + co. Necessary and sufficient conditions are given to establish the convergence in distribution of certain random variables defined by M,. When E(uf,) < co these variables play an important role toward understanding the ...

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Regularized LRT for large scale covariance matrices: One sample problem

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A Clt for Regularized Sample Covariance Matrices

Regularized estimation of large covariance matrices

A Clt for Regularized Sample Covariance Matrices by Greg

6 a Clt for Regularized Sample Covariance Matrices Greg

On the Eigenvectors of Large Dimensional Sample Covariance Matrices

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