SPARSE ESTIMATION OF LARGE COVARIANCE MATRICES VIA A NESTED LASSO PENALTY

The paper proposes a new covariance estimator for large covariance matrices when the variables have if natural ordering. Using the Cholesky decomposition of the inverse, we impose if handed 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. 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 de 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.