2012/09/11 by Han Liu, Lie Wang, Liu, Han +2
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.1209.2437
arxiv created 2012/09/11 · openalex publication_date 2012/09/11 · arxiv updated 2012/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts 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 is simultaneously minimax optimal for precision matrix estimation under different norms. Empirically, we illustrate the advantages of our method using thorough simulated and real examples. The R package bigmatrix implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran.r-project.org/.