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August 2020 Minimax estimation of large precision matrices with bandable Cholesky factor
Yu Liu, Zhao Ren
Ann. Statist. 48(4): 2428-2454 (August 2020). DOI: 10.1214/19-AOS1893


The last decade has witnessed significant methodological and theoretical advances in estimating large precision matrices. In particular, there are scientific applications such as longitudinal data, meteorology and spectroscopy in which the ordering of the variables can be interpreted through a bandable structure on the Cholesky factor of the precision matrix. However, the minimax theory has still been largely unknown, as opposed to the well established minimax results over the corresponding bandable covariance matrices. In this paper we focus on two commonly used types of parameter spaces and develop the optimal rates of convergence under both the operator norm and the Frobenius norm. A striking phenomenon is found. Two types of parameter spaces are fundamentally different under the operator norm but enjoy the same rate optimality under the Frobenius norm which is in sharp contrast to the equivalence of corresponding two types of bandable covariance matrices under both norms. This fundamental difference is established by carefully constructing the corresponding minimax lower bounds. Two new estimation procedures are developed. For the operator norm our optimal procedure is based on a novel local cropping estimator, targeting on all principle submatrices of the precision matrix, while for the Frobenius norm our optimal procedure relies on a delicate regression-based thresholding rule. Lepski’s method is considered to achieve optimal adaptation. We further establish rate optimality in the nonparanormal model. Numerical studies are carried out to confirm our theoretical findings.


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Yu Liu. Zhao Ren. "Minimax estimation of large precision matrices with bandable Cholesky factor." Ann. Statist. 48 (4) 2428 - 2454, August 2020.


Received: 1 February 2018; Revised: 1 January 2019; Published: August 2020
First available in Project Euclid: 14 August 2020

MathSciNet: MR4134801
Digital Object Identifier: 10.1214/19-AOS1893

Primary: 62H12
Secondary: 62C20, 62F12, 62G09

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.48 • No. 4 • August 2020
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