RSNet is an R package that applies resampling strategies to robustly estimate Gaussian partial correlation networks and conditional Gaussian Bayesian networks from high-dimensional mixed data while adding efficient signed graphlet degree vector analysis for interpretability.
Honest confidence regions and optimality in high-dimensional precision matrix estimation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We propose methodology for estimation of sparse precision matrices and statistical inference for their low-dimensional parameters in a high-dimensional setting where the number of parameters $p$ can be much larger than the sample size. We show that the novel estimator achieves minimax rates in supremum norm and the low-dimensional components of the estimator have a Gaussian limiting distribution. These results hold uniformly over the class of precision matrices with row sparsity of small order $\sqrt{n}/\log p$ and spectrum uniformly bounded, under a sub-Gaussian tail assumption on the margins of the true underlying distribution. Consequently, our results lead to uniformly valid confidence regions for low-dimensional parameters of the precision matrix. Thresholding the estimator leads to variable selection without imposing irrepresentability conditions. The performance of the method is demonstrated in a simulation study and on real data.
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data
RSNet is an R package that applies resampling strategies to robustly estimate Gaussian partial correlation networks and conditional Gaussian Bayesian networks from high-dimensional mixed data while adding efficient signed graphlet degree vector analysis for interpretability.