Sparse directed acyclic graphs incorporating the covariates
From MaRDI portal
Publication:2208417
DOI10.1007/s00362-018-1027-8zbMath1452.62403OpenAlexW2887961140MaRDI QIDQ2208417
Publication date: 2 November 2020
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-018-1027-8
Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)
Related Items
Uses Software
Cites Work
- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs
- Sparse inverse covariance estimation with the graphical lasso
- \(\ell_{0}\)-penalized maximum likelihood for sparse directed acyclic graphs
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Sparsistency and rates of convergence in large covariance matrix estimation
- Causation, prediction, and search
- A Bayesian method for the induction of probabilistic networks from data
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Gemini: graph estimation with matrix variate normal instances
- Pathwise coordinate optimization
- Coordinate descent algorithms for lasso penalized regression
- High-dimensional graphs and variable selection with the Lasso
- PenPC : A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
- Statistical mechanics of complex networks
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- A sparse ising model with covariates
- Model selection and estimation in the Gaussian graphical model
- Introduction to Graphical Modelling
- Sparse Matrix Graphical Models
- Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Beitrag zur Theorie des Ferromagnetismus
- Partial Correlation Estimation by Joint Sparse Regression Models
- Identifiability of Gaussian structural equation models with equal error variances
- Covariate-adjusted precision matrix estimation with an application in genetical genomics
- Identifiability of directed Gaussian graphical models with one latent source
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item