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65
Sparse Permutation Invariant Covariance Estimation
 Electronic Journal of Statistics
, 2008
"... The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in highdimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lassotype penalty. We establish a rate of convergence in the Fro ..."
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Cited by 83 (5 self)
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The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in highdimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lassotype penalty. We establish a rate of convergence in the Frobenius norm as both data dimension p and sample size n are allowed to grow, and show that the rate depends explicitly on how sparse the true concentration matrix is. We also show that a correlationbased version of the method exhibits better rates in the operator norm. The estimator is required to be positive definite, but we avoid having to use semidefinite programming by reparameterizing the objective function
A unified framework for highdimensional analysis of Mestimators with decomposable regularizers
"... ..."
Sparsistency and rates of convergence in large covariance matrices estimation
, 2007
"... This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probabi ..."
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Cited by 43 (5 self)
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This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, its inverse or its Cholesky decomposition. We study these three sparsity exploration problems under a unified framework with a general penalty function. We show that the rates of convergence for these problems under the Frobenius norm are of order (sn log pn/n) 1/2, where sn is the number of nonzero elements, pn is the size of the covariance matrix and n is the sample size. This explicitly spells out the contribution of highdimensionality is merely of a logarithmic factor. The conditions on the rate with which the tuning parameter λn goes to 0 have been made explicit and compared under different penalties. As a result, for the L1penalty, to guarantee the sparsistency and optimal rate of convergence, the number of nonzero elements should be small: s ′ n = O(pn) at most, among O(p2 n) parameters, for estimating sparse covariance or correlation matrix, sparse precision or inverse correlation matrix or sparse Cholesky factor, where s ′ n is the number of the nonzero elements on the offdiagonal entries. On the other hand, using the SCAD or hardthresholding penalty functions, there is no such a restriction. 1. Introduction. Covariance
Highdimensional covariance estimation by minimizing ℓ1penalized logdeterminant
, 2008
"... divergence ..."
Optimal rates of convergence for covariance matrix estimation
 Ann. Statist
, 2010
"... Covariance matrix plays a central role in multivariate statistical analysis. Significant advances have been made recently on developing both theory and methodology for estimating large covariance matrices. However, a minimax theory has yet been developed. In this paper we establish the optimal rates ..."
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Cited by 27 (5 self)
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Covariance matrix plays a central role in multivariate statistical analysis. Significant advances have been made recently on developing both theory and methodology for estimating large covariance matrices. However, a minimax theory has yet been developed. In this paper we establish the optimal rates of convergence for estimating the covariance matrix under both the operator norm and Frobenius norm. It is shown that optimal procedures under the two norms are different and consequently matrix estimation under the operator norm is fundamentally different from vector estimation. The minimax upper bound is obtained by constructing a special class of tapering estimators and by studying their risk properties. A key step in obtaining the optimal rate of convergence is the derivation of the minimax lower bound. The technical analysis requires new ideas that are quite different from those used in the more conventional function/sequence estimation problems. 1. Introduction. Suppose
Operator norm consistent estimation of largedimensional sparse covariance matrices
 Annals of Statistics
"... Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices X of dimension n×p, where p and n are both large. Results from random matrix theory show very clearly that in this setting, standard es ..."
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Cited by 22 (0 self)
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Estimating covariance matrices is a problem of fundamental importance in multivariate statistics. In practice it is increasingly frequent to work with data matrices X of dimension n×p, where p and n are both large. Results from random matrix theory show very clearly that in this setting, standard estimators like the sample covariance matrix perform in general very poorly. In this “large n, large p ” setting, it is sometimes the case that practitioners are willing to assume that many elements of the population covariance matrix are equal to 0, and hence this matrix is sparse. We develop an estimator to handle this situation. The estimator is shown to be consistent in operator norm, when, for instance, we have p ≍ n as n → ∞. In other words the largest singular value of the difference between the estimator and the population covariance matrix goes to zero. This implies consistency of all the eigenvalues and consistency of eigenspaces associated to isolated eigenvalues. We also propose a notion of sparsity for matrices, that is, “compatible” with spectral analysis and is independent of the ordering of the variables. 1. Introduction. Estimating
Latent Variable Graphical Model Selection via Convex Optimization
, 2010
"... Suppose we have samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of hidden components, and to learn a statistic ..."
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Cited by 22 (2 self)
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Suppose we have samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of hidden components, and to learn a statistical model over the entire collection of variables? We address this question in the setting in which the latent and observed variables are jointly Gaussian, with the conditional statistics of the observed variables conditioned on the latent variables being specified by a graphical model. As a first step we give natural conditions under which such latentvariable Gaussian graphical models are identifiable given marginal statistics of only the observed variables. Essentially these conditions require that the conditional graphical model among the observed variables is sparse, while the effect of the latent variables is “spread out ” over most of the observed variables. Next we propose a tractable convex program based on regularized maximumlikelihood for model selection in this latentvariable setting; the regularizer uses both the ℓ1 norm and the nuclear norm. Our modeling framework can be viewed as a combination of dimensionality reduction (to identify latent variables) and graphical modeling (to capture remaining statistical structure not attributable to the latent variables), and it consistently estimates both the number of hidden components and the conditional graphical model structure among the observed variables. These results are applicable in the highdimensional setting in which the number of latent/observed variables grows with the number of samples of the observed variables. The geometric properties of the algebraic varieties of sparse matrices and of lowrank matrices play an important role in our analysis.
Generalized thresholding of large covariance matrices
 J. Amer. Statist. Assoc. (Theory and Methods
, 2009
"... We propose a new class of generalized thresholding operators which combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions. Generalized thresholding of the covariance matrix has good theoretical properties and carries almost no compu ..."
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Cited by 21 (2 self)
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We propose a new class of generalized thresholding operators which combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions. Generalized thresholding of the covariance matrix has good theoretical properties and carries almost no computational burden. We obtain an explicit convergence rate in the operator norm that shows the tradeoff between the sparsity of the true model, dimension, and the sample size, and show that generalized thresholding is consistent over a large class of models as long as the dimension p and the sample size n satisfy log p/n → 0. In addition, we show
Noisy matrix decomposition via convexrelaxation: Optimal rates in high dimensions
 Annals of Statistics,40(2):1171
"... We analyze a class of estimators based on convex relaxation for solving highdimensional matrix decomposition problems. The observations are noisy realizations of a linear transformation X of the sum of an (approximately) low rank matrix � ⋆ with a second matrix Ɣ ⋆ endowed with a complementary for ..."
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Cited by 20 (7 self)
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We analyze a class of estimators based on convex relaxation for solving highdimensional matrix decomposition problems. The observations are noisy realizations of a linear transformation X of the sum of an (approximately) low rank matrix � ⋆ with a second matrix Ɣ ⋆ endowed with a complementary form of lowdimensional structure; this setup includes many statistical models of interest, including factor analysis, multitask regression and robust covariance estimation. We derive a general theorem that bounds the Frobenius norm error for an estimate of the pair ( � ⋆,Ɣ ⋆ ) obtained by solving a convex optimization problem that combines the nuclear norm with a general decomposable regularizer. Our results use a “spikiness ” condition that is related to, but milder than, singular vector incoherence. We specialize our general result to two cases that have been studied in past work: low rank plus an entrywise sparse matrix, and low rank plus a columnwise sparse matrix. For both models, our theory yields nonasymptotic Frobenius error bounds for both deterministic and stochastic noise matrices, and applies to matrices � ⋆ that can be exactly or approximately low rank, and matrices Ɣ ⋆ that can be exactly or approximately sparse. Moreover, for the case of stochastic noise matrices and the identity observation operator, we establish matching lower bounds on the minimax error. The sharpness of our nonasymptotic predictions is confirmed by numerical simulations. 1. Introduction. The
Tibshirani R: Covarianceregularized regression and and classification for highdimensional problems
 Journal of Royal Statistical Society, Series B
"... Summary. In recent years, many methods have been developed for regression in highdimensional settings. We propose covarianceregularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate ..."
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Cited by 15 (3 self)
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Summary. In recent years, many methods have been developed for regression in highdimensional settings. We propose covarianceregularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing its log likelihood, under a multivariate normal model, subject to a constraint on its elements; this estimate is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso, and the elastic net are special cases of covarianceregularized regression, and we demonstrate that certain previously unexplored forms of covarianceregularized regression can outperform existing methods in a range of situations. The covarianceregularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyze gene expression data sets with multiple class and survival outcomes.