TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
Name
euclid.ejs.1486371613.pdf
Size
1.22 MB
Format
Adobe PDF
Checksum (MD5)
2cef6dc482e3a239f5b04d2023f71e42
Author(s)
Liu, Han
Wang, Lie
Date Issued
February 2017
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Attribution 2.5 Generic (CC BY 2.5)
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1214/16-EJS1195
