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20180806 Zhao Ren:Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor
时间:2018-08-06

时间:8月6日(周一)14:30

地点:明德主楼 1016 会议室

题目:Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor


摘要:Last decade witnesses 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 block-thresholding rule. Adaptive estimation is achieved using Lepski’s method. We further establish rate optimality in the nonparanormal model. Numerical studies are carried out to confirm our theoretical findings.


简介:Zhao Ren is an Assistant Professor in the Department of Statistics at the University of Pittsburgh. Prior to joining Pitt, Dr. Ren obtained his Ph.D. in Statistics at Yale University in 2014. He is broadly interested in high-dimensional statistical inference, covariance / precision matrix estimation, graphical models, statistical machine learning, nonparametric function estimation, robust covariance estimation and applications in statistical genomics.