ROCKET: robust confidence intervals via Kendall's tau for transelliptical graphical models
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Publication:1990586
DOI10.1214/17-AOS1663zbMath1410.62059arXiv1502.07641OpenAlexW2963611525MaRDI QIDQ1990586
Rina Foygel Barber, Mladen Kolar
Publication date: 25 October 2018
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1502.07641
graphical model selectioncovariance selectionpost-model selection inferencerank-based estimationtranselliptical graphical modelsuniformly valid inference
Asymptotic properties of parametric estimators (62F12) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35)
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Uses Software
Cites Work
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- 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
- Gaussian graphical model estimation with false discovery rate control
- Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas
- Exact post-selection inference, with application to the Lasso
- Robust inference on average treatment effects with possibly more covariates than observations
- TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
- Statistics for high-dimensional data. Methods, theory and applications.
- Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
- Regularized rank-based estimation of high-dimensional nonparanormal graphical models
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Sparsistency and rates of convergence in large covariance matrix estimation
- High-dimensional semiparametric Gaussian copula graphical models
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Least squares after model selection in high-dimensional sparse models
- ROCKET: robust confidence intervals via Kendall's tau for transelliptical graphical models
- A significance test for the lasso
- Network exploration via the adaptive LASSO and SCAD penalties
- 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
- On Graphical Models via Univariate Exponential Family Distributions
- Sparse Matrix Inversion with Scaled Lasso
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Joint estimation of multiple graphical models
- Model selection and estimation in the Gaussian graphical model
- Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case
- First-Order Methods for Sparse Covariance Selection
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Quantile regression for competing risks data with missing cause of failure
- State evolution for general approximate message passing algorithms, with applications to spatial coupling
- Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
- Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
