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

Han Liu, Lie Wang

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)241-294
Number of pages54
JournalElectronic Journal of Statistics
Issue number1
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'TIGER: A tuning-insensitive approach for optimally estimating gaussian graphical models'. Together they form a unique fingerprint.

Cite this