Pages that link to "Item:Q1951760"

The following pages link to Sparse permutation invariant covariance estimation (Q1951760):

Displaying 50 items.

• Missing values: sparse inverse covariance estimation and an extension to sparse regression (Q80804) (← links)
• A general algorithm for covariance modeling of discrete data (Q113808) (← links)
• Estimating sufficient reductions of the predictors in abundant high-dimensional regressions (Q116954) (← links)
• Confidence intervals for high-dimensional inverse covariance estimation (Q117382) (← links)
• Latent variable graphical model selection via convex optimization (Q132216) (← links)
• Asymptotic normality and optimalities in estimation of large Gaussian graphical models (Q152845) (← links)
• Honest confidence regions and optimality in high-dimensional precision matrix estimation (Q152848) (← links)
• Gaussian graphical model estimation with false discovery rate control (Q152850) (← links)
• Scaling it up: stochastic search structure learning in graphical models (Q273600) (← links)
• Estimating sparse precision matrix: optimal rates of convergence and adaptive estimation (Q282440) (← links)
• Gaussian and robust Kronecker product covariance estimation: existence and uniqueness (Q290708) (← links)
• Joint estimation of precision matrices in heterogeneous populations (Q302425) (← links)
• Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks (Q306638) (← links)
• Prediction in abundant high-dimensional linear regression (Q391850) (← links)
• High-dimensional covariance matrix estimation with missing observations (Q395991) (← links)
• Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood (Q444979) (← links)
• Covariance estimation: the GLM and regularization perspectives (Q449843) (← links)
• Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis (Q458641) (← links)
• Posterior convergence rates for estimating large precision matrices using graphical models (Q470497) (← links)
• Estimation of high-dimensional partially-observed discrete Markov random fields (Q470504) (← links)
• Optimal computational and statistical rates of convergence for sparse nonconvex learning problems (Q482875) (← links)
• On the existence of the weighted bridge penalized Gaussian likelihood precision matrix estimator (Q485922) (← links)
• Reduced-rank multi-label classification (Q517398) (← links)
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling (Q548547) (← links)
• High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: risk underestimation (Q620558) (← links)
• High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity (Q693741) (← links)
• Regularized rank-based estimation of high-dimensional nonparanormal graphical models (Q741796) (← links)
• Improved multivariate normal mean estimation with unknown covariance when \(p\) is greater than \(n\) (Q741819) (← links)
• Estimating heterogeneous graphical models for discrete data with an application to roll call voting (Q746672) (← links)
• A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions (Q829737) (← links)
• Promote sign consistency in the joint estimation of precision matrices (Q830115) (← links)
• Ensemble sparse estimation of covariance structure for exploring genetic disease data (Q830118) (← links)
• Efficient estimation of approximate factor models via penalized maximum likelihood (Q898581) (← links)
• Estimation of the inverse scatter matrix of an elliptically symmetric distribution (Q900790) (← links)
• Shrinkage and model selection with correlated variables via weighted fusion (Q961274) (← links)
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression (Q973867) (← links)
• Adaptive estimation of stationary Gaussian fields (Q973870) (← links)
• Estimating time-varying networks (Q977626) (← links)
• Optimal rates of convergence for covariance matrix estimation (Q988000) (← links)
• Transposable regularized covariance models with an application to missing data imputation (Q993250) (← links)
• Covariance regularization by thresholding (Q1000302) (← links)
• Sparsistency and rates of convergence in large covariance matrix estimation (Q1043730) (← links)
• A joint convex penalty for inverse covariance matrix estimation (Q1623469) (← links)
• The cluster graphical Lasso for improved estimation of Gaussian graphical models (Q1623817) (← links)
• Adjusted regularization in latent graphical models: application to multiple-neuron spike count data (Q1624826) (← links)
• Adjusted regularization of cortical covariance (Q1628355) (← links)
• Sparse estimation of high-dimensional correlation matrices (Q1660228) (← links)
• Joint estimation of multiple Gaussian graphical models across unbalanced classes (Q1662174) (← links)
• A constrained \(\ell1\) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (Q1698844) (← links)
• Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer (Q1727851) (← links)