title: Convex Banding of the Covariance Matrix.
abstract: We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.
authors.affiliation: Cornell University, Department of Biological Statistics and Computational Biology, 1178 Comstock Hall, Cornell University, Ithaca, 14853 United States.
authors.forename: Jacob
authors.initials: J
authors.lastname: Bien
authors.affiliation: Cornell University, Statistical Science, Ithaca, 14850 United States.
authors.forename: Florentina
authors.initials: F
authors.lastname: Bunea
authors.affiliation: Johns Hopkins University, Department of Biostatistics, Baltimore, United States.
authors.forename: Luo
authors.initials: L
authors.lastname: Xiao
keywords: High-dimensional; adaptive; hierarchical group lasso; structured sparsity