Estimating Sparse Precision Matrix: Optimal Rates of Convergence and Adaptive Estimation

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Statistics Papers
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Constrained ℓ1-minimization
covariance matrix
graphical model
minimax lower bound
optimal rate of convergence
precision matrix
sparsity
spectral norm.
Physical Sciences and Mathematics
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Cai, Tony
Liu, Weidong
Zhou, Harrison H
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Precision matrix is of significant importance in a wide range of applications in multivariate analysis. This paper considers adaptive minimax estimation of sparse precision matrices in the high dimensional setting. Optimal rates of convergence are established for a range of matrix norm losses. A fully data driven estimator based on adaptive constrained ℓ1 minimization is proposed and its rate of convergence is obtained over a collection of parameter spaces. The estimator, called ACLIME, is easy to implement and performs well numerically. A major step in establishing the minimax rate of convergence is the derivation of a rate-sharp lower bound. A “two-directional” lower bound technique is applied to obtain the minimax lower bound. The upper and lower bounds together yield the optimal rates of convergence for sparse precision matrix estimation and show that the ACLIME estimator is adaptively minimax rate optimal for a collection of parameter spaces and a range of matrix norm losses simultaneously.

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2016-03-01
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The Annals of Statistics
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