Pages that link to "Item:Q2346526"

The following pages link to Preconditioning the Lasso for sign consistency (Q2346526):

Displaying 19 items.

• Orthogonal one step greedy procedure for heteroscedastic linear models (Q254223) (← links)
• High-dimensional model recovery from random sketched data by exploring intrinsic sparsity (Q782446) (← links)
• Promote sign consistency in the joint estimation of precision matrices (Q830115) (← links)
• Change-point estimation in the multivariate model taking into account the dependence: application to the vegetative development of oilseed rape (Q1618099) (← links)
• On stepwise pattern recovery of the fused Lasso (Q1660156) (← links)
• On the total variation regularized estimator over a class of tree graphs (Q1711590) (← links)
• A partially proximal linearized alternating minimization method for finding Dantzig selectors (Q1983897) (← links)
• Doubly debiased Lasso: high-dimensional inference under hidden confounding (Q2148976) (← links)
• Adaptive multi-penalty regularization based on a generalized Lasso path (Q2175012) (← links)
• Sparse identification of truncation errors (Q2222522) (← links)
• Preconditioning the Lasso for sign consistency (Q2346526) (← links)
• Recovery of partly sparse and dense signals (Q2692936) (← links)
• Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs (Q5027037) (← links)
• Semi-Standard Partial Covariance Variable Selection When Irrepresentable Conditions Fail (Q5041338) (← links)
• (Q5149025) (← links)
• Path algorithms for fused lasso signal approximator with application to COVID‐19 spread in Korea (Q6089887) (← links)
• Variable selection in Bayesian multiple instance regression using shotgun stochastic search (Q6573299) (← links)
• Variable selection for generalized linear model with highly correlated covariates (Q6580088) (← links)
• DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation (Q6631694) (← links)