Pages that link to "Item:Q961274"

The following pages link to Shrinkage and model selection with correlated variables via weighted fusion (Q961274):

Displaying 19 items.

• The sparse Laplacian shrinkage estimator for high-dimensional regression (Q651021) (← links)
• Practical variable selection for generalized additive models (Q901636) (← links)
• Regression adjustment for treatment effect with multicollinearity in high dimensions (Q1727920) (← links)
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods (Q1952223) (← links)
• Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures (Q2101407) (← links)
• Sparse Laplacian shrinkage with the graphical Lasso estimator for regression problems (Q2125484) (← links)
• Penalized regression combining the \( L_{1}\) norm and a correlation based penalty (Q2253826) (← links)
• An extended variable inclusion and shrinkage algorithm for correlated variables (Q2359516) (← links)
• Special issue on variable selection and robust procedures (Q2445742) (← links)
• Graph structured sparse subset selection (Q2662712) (← links)
• The Adaptive Gril Estimator with a Diverging Number of Parameters (Q2859305) (← links)
• Group Variable Selection with Oracle Property by Weight-Fused Adaptive Elastic Net Model for Strongly Correlated Data (Q2876160) (← links)
• Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data (Q2905731) (← links)
• Correlated model fusion (Q4620236) (← links)
• Group variable selection for data with dependent structures (Q4913930) (← links)
• Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs (Q5027037) (← links)
• Generalised regression estimators for average treatment effect with multicollinearity in high-dimensional covariates (Q5078829) (← links)
• (Q5488713) (← links)
• Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders (Q6628719) (← links)