Pages that link to "Item:Q2813897"

The following pages link to Efficient estimation of covariance selection models (Q2813897):

Displaying 50 items.

• Sparse estimation of a covariance matrix (Q123825) (← links)
• Sparse inverse covariance estimation with the graphical lasso (Q150076) (← links)
• High dimensional covariance matrix estimation using a factor model (Q299275) (← links)
• Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances (Q391528) (← links)
• Reference priors for linear models with general covariance structures (Q433785) (← links)
• Covariance estimation: the GLM and regularization perspectives (Q449843) (← links)
• Bayesian sparse graphical models for classification with application to protein expression data (Q484003) (← links)
• Expected predictive least squares for model selection in covariance structures (Q512004) (← links)
• Bayesian graphical models for differential pathways (Q516442) (← links)
• Learning Gaussian graphical models with fractional marginal pseudo-likelihood (Q518603) (← links)
• An inexact interior point method for \(L_{1}\)-regularized sparse covariance selection (Q621755) (← links)
• Copula Gaussian graphical models and their application to modeling functional disability data (Q641151) (← links)
• A block coordinate gradient descent method for regularized convex separable optimization and covariance selection (Q644904) (← links)
• The cost of using decomposable Gaussian graphical models for computational convenience (Q693257) (← links)
• An empirical Bayes procedure for the selection of Gaussian graphical models (Q693346) (← links)
• Constructing priors based on model size for nondecomposable Gaussian graphical models: a simulation based approach (Q716165) (← links)
• Covariance selection and multivariate dependence (Q765841) (← links)
• The Bayesian covariance lasso (Q897177) (← links)
• The performance of covariance selection methods that consider decomposable models only (Q899046) (← links)
• Distribution of random correlation matrices: hyperspherical parameterization of the Cholesky factor (Q900522) (← links)
• Shrinkage and model selection with correlated variables via weighted fusion (Q961274) (← links)
• Estimation of covariance matrix via the sparse Cholesky factor with lasso (Q993832) (← links)
• Modeling covariance matrices via partial autocorrelations (Q1036800) (← links)
• Sparsistency and rates of convergence in large covariance matrix estimation (Q1043730) (← links)
• Covariate selection from telematics car driving data (Q1707549) (← links)
• Efficient Bayesian regularization for graphical model selection (Q1738143) (← links)
• Model uncertainty (Q1766316) (← links)
• A class of shrinkage priors for the dependence structure in longitudinal data (Q1888833) (← links)
• An improved estimation to make Markowitz's portfolio optimization theory users friendly and estimation accurate with application on the US stock market investment (Q1926915) (← links)
• Fitting very large sparse Gaussian graphical models (Q1927038) (← links)
• Adaptive covariance estimation with model selection (Q1935400) (← links)
• Sparse permutation invariant covariance estimation (Q1951760) (← links)
• High dimensional sparse covariance estimation via directed acyclic graphs (Q1952020) (← links)
• Bayesian Lasso with neighborhood regression method for Gaussian graphical model (Q2013049) (← links)
• Joint mean-covariance estimation via the horseshoe (Q2022549) (← links)
• Copula shrinkage and portfolio allocation in ultra-high dimensions (Q2098001) (← links)
• Efficient sampling in spectrahedra and volume approximation (Q2144244) (← links)
• Parametrising correlation matrices (Q2181727) (← links)
• Bayesian inference in nonparanormal graphical models (Q2226690) (← links)
• Network exploration via the adaptive LASSO and SCAD penalties (Q2270657) (← links)
• Bayesian estimation of large precision matrix based on Cholesky decomposition (Q2311706) (← links)
• An efficiency upper bound for inverse covariance estimation (Q2351738) (← links)
• Experiments in stochastic computation for high-dimensional graphical models (Q2381758) (← links)
• Model selection strategies for identifying most relevant covariates in homoscedastic linear models (Q2445774) (← links)
• Sparse estimation of large covariance matrices via a nested Lasso penalty (Q2482977) (← links)
• High-dimensional graphs and variable selection with the Lasso (Q2500458) (← links)
• Estimation of the multivariate normal precision and covariance matrices in a star-shape model (Q2501353) (← links)
• Bayesian sparse covariance decomposition with a graphical structure (Q2631381) (← links)
• Bayesian analysis of nonparanormal graphical models using rank-likelihood (Q2676906) (← links)
• Exact distribution of the MLE of concentration matrices in decomposable covariance selection models (Q2746505) (← links)