Pages that link to "Item:Q5378137"
From MaRDI portal
The following pages link to A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees (Q5378137):
Displaying 37 items.
- Bayesian regularization of Gaussian graphical models with measurement error (Q830423) (← links)
- Conditional score matching for high-dimensional partial graphical models (Q830589) (← links)
- Inferring large graphs using \(\ell_1\)-penalized likelihood (Q1704026) (← links)
- Optimal estimation of a large-dimensional covariance matrix under Stein's loss (Q1750102) (← links)
- Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models (Q1752364) (← links)
- A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data (Q2008637) (← links)
- Some aspects of response variable selection and estimation in multivariate linear regression (Q2062774) (← links)
- Positive-definite modification of a covariance matrix by minimizing the matrix \(\ell_{\infty}\) norm with applications to portfolio optimization (Q2068898) (← links)
- High-dimensional correlation matrix estimation for general continuous data with Bagging technique (Q2102349) (← links)
- An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units (Q2135867) (← links)
- A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions (Q2152553) (← links)
- Contraction of a quasi-Bayesian model with shrinkage priors in precision matrix estimation (Q2156815) (← links)
- Envelope-based sparse partial least squares (Q2176612) (← links)
- Bayesian discriminant analysis using a high dimensional predictor (Q2316972) (← links)
- Multivariate Gaussian network structure learning (Q2317263) (← links)
- Loss function, unbiasedness, and optimality of Gaussian graphical model selection (Q2317292) (← links)
- Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage (Q2418516) (← links)
- Kronecker-structured covariance models for multiway data (Q2678238) (← links)
- Statistical inference via conditional Bayesian posteriors in high-dimensional linear regression (Q2689601) (← links)
- Edge selection for undirected graphs (Q4960765) (← links)
- High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators (Q5107390) (← links)
- Graph-Guided Banding of the Covariance Matrix (Q5231506) (← links)
- Bayesian Regularization for Graphical Models With Unequal Shrinkage (Q5242470) (← links)
- (Q5381131) (← links)
- Learning Gaussian graphical models with latent confounders (Q6051077) (← links)
- A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models (Q6069494) (← links)
- Covariance structure estimation with Laplace approximation (Q6074739) (← links)
- Estimation of Gaussian directed acyclic graphs using partial ordering information with applications to DREAM3 networks and dairy cattle data (Q6104082) (← links)
- An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled Lasso (Q6104410) (← links)
- Analysis of air quality time series of Hong Kong with graphical modeling (Q6179624) (← links)
- Robust and sparse Gaussian graphical modelling under cell-wise contamination (Q6541453) (← links)
- Development of network-guided transcriptomic risk score for disease prediction (Q6548919) (← links)
- Response variable selection in multivariate linear regression (Q6593365) (← links)
- Estimation of graphical models: an overview of selected topics (Q6612364) (← links)
- Consistent skinny Gibbs in probit regression (Q6626714) (← links)
- Assisted graphical model for gene expression data analysis (Q6627100) (← links)
- Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19 (Q6629308) (← links)
