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.
- Discussion: Latent variable graphical model selection via convex optimization (Q5970646) (← links)
- Discussion: Latent variable graphical model selection via convex optimization (Q5970647) (← links)
- Discussion: Latent variable graphical model selection via convex optimization (Q5970648) (← links)
- Sample average approximation with heavier tails II: localization in stochastic convex optimization and persistence results for the Lasso (Q6038638) (← links)
- Variable selection, monotone likelihood ratio and group sparsity (Q6042348) (← links)
- Variable selection for high‐dimensional generalized linear model with block‐missing data (Q6049794) (← links)
- Learning Gaussian graphical models with latent confounders (Q6051077) (← links)
- A random covariance model for bi‐level graphical modeling with application to resting‐state fMRI data (Q6055494) (← links)
- Generalized Network Structured Models with Mixed Responses Subject to Measurement Error and Misclassification (Q6055854) (← links)
- Inference for Nonparanormal Partial Correlation via Regularized Rank-Based Nodewise Regression (Q6055865) (← links)
- Sparse multivariate regression with missing values and its application to the prediction of material properties (Q6061744) (← links)
- Horseshoe Regularisation for Machine Learning in Complex and Deep Models<sup>1</sup> (Q6064351) (← links)
- Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models (Q6066549) (← links)
- A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates (Q6067162) (← links)
- A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models (Q6069494) (← links)
- Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional Covariates (Q6069869) (← links)
- A Unified Framework for Change Point Detection in High-Dimensional Linear Models (Q6069892) (← links)
- Extending compositional data analysis from a graph signal processing perspective (Q6074725) (← links)
- Analysis of noisy survival data with graphical proportional hazards measurement error models (Q6076507) (← links)
- Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model (Q6077536) (← links)
- Frequentist Model Averaging for Undirected Gaussian Graphical Models (Q6079689) (← links)
- Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-Variate fMRI Data (Q6079710) (← links)
- Debiased lasso for generalized linear models with a diverging number of covariates (Q6079870) (← links)
- On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data (Q6079972) (← links)
- Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis (Q6079973) (← links)
- Structure learning of exponential family graphical model with false discovery rate control (Q6080784) (← links)
- A loss‐based prior for Gaussian graphical models (Q6081851) (← links)
- Sparse and Low-Rank Matrix Quantile Estimation With Application to Quadratic Regression (Q6086172) (← links)
- Region selection in Markov random fields: Gaussian case (Q6097551) (← links)
- Exact test theory in Gaussian graphical models (Q6097560) (← links)
- An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled Lasso (Q6104410) (← links)
- Controlling False Discovery Rate Using Gaussian Mirrors (Q6107203) (← links)
- Sharpe ratio analysis in high dimensions: residual-based nodewise regression in factor models (Q6108258) (← links)
- Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares (Q6110712) (← links)
- Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data (Q6110738) (← links)
- Block-diagonal precision matrix regularization for ultra-high dimensional data (Q6111505) (← links)
- Densely connected sub-Gaussian linear structural equation model learning via \(\ell_1\)- and \(\ell_2\)-regularized regressions (Q6113746) (← links)
- Topological techniques in model selection (Q6115956) (← links)
- Unbalanced distributed estimation and inference for the precision matrix in Gaussian graphical models (Q6116588) (← links)
- A Bayesian approach for partial Gaussian graphical models with sparsity (Q6122031) (← links)
- A power analysis for Model-X knockoffs with \(\ell_p\)-regularized statistics (Q6136579) (← links)
- Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes (Q6136582) (← links)
- Post-selection Inference of High-dimensional Logistic Regression Under Case–Control Design (Q6149873) (← links)
- A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity (Q6173730) (← links)
- A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems (Q6177008) (← links)
- Concentration of measure bounds for matrix-variate data with missing values (Q6178556) (← links)
- Subbotin graphical models for extreme value dependencies with applications to functional neuronal connectivity (Q6179133) (← links)
- Graphical models for nonstationary time series (Q6183745) (← links)
- Multiclass sparse discriminant analysis incorporating graphical structure among predictors (Q6187806) (← links)
- Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates (Q6190704) (← links)
