Pages that link to "Item:Q2500458"
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The following pages link to High-dimensional graphs and variable selection with the Lasso (Q2500458):
Displaying 50 items.
- StarTrek: combinatorial variable selection with false discovery rate control (Q6192319) (← links)
- Partial correlation graphical LASSO (Q6196791) (← links)
- Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions (Q6196794) (← links)
- A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression (Q6200883) (← links)
- Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting (Q6200890) (← links)
- High-dimensional functional graphical model structure learning via neighborhood selection approach (Q6200903) (← links)
- Model selection for inferring Gaussian graphical models (Q6204969) (← links)
- Estimation of multiple networks with common structures in heterogeneous subgroups (Q6536691) (← links)
- Bayesian sparse graphical models and their mixtures (Q6537781) (← links)
- Posterior convergence rates for high-dimensional precision matrix estimation using \(G\)-Wishart priors (Q6540514) (← links)
- Robust and sparse Gaussian graphical modelling under cell-wise contamination (Q6541453) (← links)
- Joint estimation of multiple mixed graphical models for pan-cancer network analysis (Q6541556) (← links)
- MuSP: a multistep screening procedure for sparse recovery (Q6541761) (← links)
- Development of network-guided transcriptomic risk score for disease prediction (Q6548919) (← links)
- Consistent group selection using nonlocal priors in regression (Q6549170) (← links)
- Lasso-based variable selection methods in text regression: the case of short texts (Q6549697) (← links)
- Testing the differential network between two gaussian graphical models with false discovery rate control (Q6552575) (← links)
- Optimal Linear Discriminant Analysis for High-Dimensional Functional Data (Q6567901) (← links)
- Nonlinear Causal Discovery with Confounders (Q6567913) (← links)
- Large-Scale Two-Sample Comparison of Support Sets (Q6567954) (← links)
- Multiple hypothesis testing for variable selection (Q6569948) (← links)
- On the probability of (falsely) connecting two distinct components when learning a GGM (Q6571732) (← links)
- Stress testing network reconstruction via graphical causal model (Q6579512) (← links)
- Locally sparse and robust partial least squares in scalar-on-function regression (Q6581694) (← links)
- Cardinality-constrained structured data-fitting problems (Q6584338) (← links)
- One-step sparse ridge estimation with folded concave penalty (Q6590301) (← links)
- Iterative adaptive robust variable selection in nomparametric additive models (Q6592367) (← links)
- Support recovery of Gaussian graphical model with false discovery rate control (Q6594996) (← links)
- Bayesian inference of graph-based dependencies from mixed-type data (Q6596172) (← links)
- High-dimensional undirected graphical models for arbitrary mixed data (Q6597252) (← links)
- High-dimensional covariance matrix estimation (Q6601084) (← links)
- Multiple and multilevel graphical models (Q6601096) (← links)
- Joint Gaussian graphical model estimation: a survey (Q6602381) (← links)
- Algorithm 1045: a covariate-dependent approach to Gaussian graphical modeling in R (Q6604172) (← links)
- The cluster D-trace loss for differential network analysis (Q6604246) (← links)
- Least angle regression for model selection (Q6604388) (← links)
- Use of majority votes in statistical learning (Q6604473) (← links)
- Robustly fitting Gaussian graphical models -- the R package robFitConGraph (Q6606410) (← links)
- Asymptotic behaviour of penalized robust estimators in logistic regression when dimension increases (Q6606412) (← links)
- A review of multivariate distributions for count data derived from the Poisson distribution (Q6607052) (← links)
- Estimation of covariance and precision matrix, network structure, and a view toward systems biology (Q6607066) (← links)
- Estimation of graphical models: an overview of selected topics (Q6612364) (← links)
- Overview of research advance for knockoff methods (Q6615097) (← links)
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing (Q6615922) (← links)
- Scalable multiple network inference with the joint graphical horseshoe (Q6616331) (← links)
- A Nodewise Regression Approach to Estimating Large Portfolios (Q6617775) (← links)
- Differential network knockoff filter with application to brain connectivity analysis (Q6618396) (← links)
- Community Detection in Partial Correlation Network Models (Q6620846) (← links)
- A simple method for estimating Gaussian graphical models (Q6621324) (← links)
- One-step regularized estimator for high-dimensional regression models (Q6621326) (← links)
