Pages that link to "Item:Q3512676"

The following pages link to Model selection and estimation in the Gaussian graphical model (Q3512676):

Displaying 50 items.

• Inferring multiple graphical structures (Q637986) (← links)
• Robust graphical modeling of gene networks using classical and alternative \(t\)-distributions (Q641155) (← links)
• The sparse Laplacian shrinkage estimator for high-dimensional regression (Q651021) (← links)
• Distribution-free tests of mean vectors and covariance matrices for multivariate paired data (Q715492) (← links)
• Constructing priors based on model size for nondecomposable Gaussian graphical models: a simulation based approach (Q716165) (← links)
• Regularized rank-based estimation of high-dimensional nonparanormal graphical models (Q741796) (← links)
• Edge selection based on the geometry of dually flat spaces for Gaussian graphical models (Q746349) (← links)
• Estimating heterogeneous graphical models for discrete data with an application to roll call voting (Q746672) (← links)
• Lasso-driven inference in time and space (Q820826) (← links)
• An integrated framework for visualizing and forecasting realized covariance matrices (Q825351) (← links)
• Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances (Q829714) (← links)
• Ensemble sparse estimation of covariance structure for exploring genetic disease data (Q830118) (← links)
• Bayesian regularization of Gaussian graphical models with measurement error (Q830423) (← links)
• An adapted linear discriminant analysis with variable selection for the classification in high-dimension, and an application to medical data (Q830539) (← links)
• Conditional score matching for high-dimensional partial graphical models (Q830589) (← links)
• High-dimensional analysis of semidefinite relaxations for sparse principal components (Q834367) (← links)
• High dimensional posterior convergence rates for decomposable graphical models (Q902216) (← links)
• A note on the Lasso for Gaussian graphical model selection (Q927362) (← links)
• Tests for Gaussian graphical models (Q961382) (← links)
• Least angle and \(\ell _{1}\) penalized regression: a review (Q975564) (← links)
• Estimating time-varying networks (Q977626) (← links)
• Estimation of covariance matrix via the sparse Cholesky factor with lasso (Q993832) (← links)
• Covariance regularization by thresholding (Q1000302) (← links)
• Regularized parameter estimation of high dimensional distribution (Q1015875) (← links)
• On the distribution of the adaptive LASSO estimator (Q1022011) (← links)
• Sparsistency and rates of convergence in large covariance matrix estimation (Q1043730) (← links)
• Edge detection in sparse Gaussian graphical models (Q1615220) (← links)
• A general family of trimmed estimators for robust high-dimensional data analysis (Q1616324) (← links)
• Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks (Q1621019) (← 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)
• Recent developments in high dimensional covariance estimation and its related issues, a review (Q1657856) (← links)
• High dimensional Gaussian copula graphical model with FDR control (Q1658182) (← links)
• High dimensional covariance matrix estimation by penalizing the matrix-logarithm transformed likelihood (Q1658345) (← links)
• Ridge estimation of inverse covariance matrices from high-dimensional data (Q1659004) (← links)
• Robust estimation of precision matrices under cellwise contamination (Q1660231) (← links)
• Joint estimation of multiple Gaussian graphical models across unbalanced classes (Q1662174) (← links)
• Estimating large covariance matrix with network topology for high-dimensional biomedical data (Q1663109) (← links)
• Confidence regions for entries of a large precision matrix (Q1668572) (← links)
• A constrained \(\ell1\) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (Q1698844) (← links)
• Inferring large graphs using \(\ell_1\)-penalized likelihood (Q1704026) (← links)
• Heterogeneity adjustment with applications to graphical model inference (Q1711558) (← links)
• Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer (Q1727851) (← links)
• Efficient Bayesian regularization for graphical model selection (Q1738143) (← links)
• A multiple testing approach to the regularisation of large sample correlation matrices (Q1739875) (← links)
• Variable selection in multivariate linear models with high-dimensional covariance matrix estimation (Q1749984) (← links)
• Covariance estimation via sparse Kronecker structures (Q1750103) (← links)
• Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications (Q1750282) (← links)