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TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models

Author(s)
Liu, Han; Wang, Lie
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

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Attribution 2.5 Generic (CC BY 2.5) https://creativecommons.org/licenses/by/2.5/
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Abstract
We propose a new procedure for optimally estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: It requires very little effort to choose the tuning parameter in finite sample settings. Computationally, our procedure is significantly faster than existing methods due to its tuning-insensitive property. Theoretically, the obtained estimator simultaneously achieves minimax lower bounds for precision matrix estimation under different norms. Empirically, we illustrate the advantages of the proposed method using simulated and real examples. The R package camel implementing the proposed methods is also available on the Comprehensive R Archive Network.
Date issued
2017-02
URI
http://hdl.handle.net/1721.1/114214
Department
Massachusetts Institute of Technology. Department of Mathematics
Journal
Electronic Journal of Statistics
Publisher
Institute of Mathematical Statistics
Citation
Liu, Han, and Lie Wang. “TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models.” Electronic Journal of Statistics 11, 1 (February 2017): 241–294 © 2017 Institute of Mathematical Statistics
Version: Final published version
ISSN
1935-7524

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