Confidence intervals for high-dimensional inverse covariance estimation
From MaRDI portal
Publication:117382
DOI10.1214/15-ejs1031zbMath1328.62458arXiv1403.6752MaRDI QIDQ117382
Jana Janková, Sara Van De Geer, Sara van de Geer, Jana Janková
Publication date: 1 January 2015
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1403.6752
Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07)
Related Items
Worst possible sub-directions in high-dimensional models, Change-point detection in high-dimensional covariance structure, Partitioned Approach for High-dimensional Confidence Intervals with Large Split Sizes, Hypothesis testing for high-dimensional multivariate regression with false discovery rate control, Scalable inference for high-dimensional precision matrix, A unified theory of confidence regions and testing for high-dimensional estimating equations, High-Dimensional Inference for Cluster-Based Graphical Models, Meta-analytic Gaussian network aggregation, The benefit of group sparsity in group inference with de-biased scaled group Lasso, Unnamed Item, Unnamed Item, Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors, Debiasing the debiased Lasso with bootstrap, Unbalanced distributed estimation and inference for the precision matrix in Gaussian graphical models, A two-step method for estimating high-dimensional Gaussian graphical models, Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting, Confidence intervals for sparse precision matrix estimation via Lasso penalized D-trace loss, Combinatorial inference for graphical models, Fixed Effects Testing in High-Dimensional Linear Mixed Models, High-dimensional inference in misspecified linear models, Innovated scalable efficient inference for ultra-large graphical models, Inference of large modified Poisson-type graphical models: application to RNA-seq data in childhood atopic asthma studies, Debiasing the Lasso: optimal sample size for Gaussian designs, Unnamed Item, A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models, Efficient distributed estimation of high-dimensional sparse precision matrix for transelliptical graphical models, Semiparametric efficiency bounds for high-dimensional models, Non-asymptotic error controlled sparse high dimensional precision matrix estimation, Honest confidence regions and optimality in high-dimensional precision matrix estimation, GGMncv, SILGGM, Inference for high-dimensional varying-coefficient quantile regression, Unnamed Item, Network differential connectivity analysis, Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Sparse inverse covariance estimation with the graphical lasso
- Asymptotic normality and optimalities in estimation of large Gaussian graphical models
- Worst possible sub-directions in high-dimensional models
- Valid post-selection inference
- Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
- Statistics for high-dimensional data. Methods, theory and applications.
- High-dimensional variable selection
- Can one estimate the conditional distribution of post-model-selection estimators?
- Covariance regularization by thresholding
- Asymptotics for Lasso-type estimators.
- On the distribution of the largest eigenvalue in principal components analysis
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Confidence sets in sparse regression
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- High-dimensional graphs and variable selection with the Lasso
- Sparse Matrix Inversion with Scaled Lasso
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- p-Values for High-Dimensional Regression
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Bootstrapping Lasso Estimators
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Model selection and estimation in the Gaussian graphical model
- First-Order Methods for Sparse Covariance Selection
- MODEL SELECTION AND INFERENCE: FACTS AND FICTION
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers