The Annals of Applied Statistics

Sparse estimation of large covariance matrices via a nested Lasso penalty

Elizaveta Levina, Adam Rothman, and Ji Zhu

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The paper proposes a new covariance estimator for large covariance matrices when the variables have a natural ordering. Using the Cholesky decomposition of the inverse, we impose a banded structure on the Cholesky factor, and select the bandwidth adaptively for each row of the Cholesky factor, using a novel penalty we call nested Lasso. This structure has more flexibility than regular banding, but, unlike regular Lasso applied to the entries of the Cholesky factor, results in a sparse estimator for the inverse of the covariance matrix. An iterative algorithm for solving the optimization problem is developed. The estimator is compared to a number of other covariance estimators and is shown to do best, both in simulations and on a real data example. Simulations show that the margin by which the estimator outperforms its competitors tends to increase with dimension.

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Ann. Appl. Stat., Volume 2, Number 1 (2008), 245-263.

First available in Project Euclid: 24 March 2008

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Covariance matrix high dimension low sample size large p small n Lasso sparsity Cholesky decomposition


Levina, Elizaveta; Rothman, Adam; Zhu, Ji. Sparse estimation of large covariance matrices via a nested Lasso penalty. Ann. Appl. Stat. 2 (2008), no. 1, 245--263. doi:10.1214/07-AOAS139.

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