The following pages link to (Q3174050):
Displaying 50 items.
- The predictive Lasso (Q693339) (← links)
- Nonconcave penalized composite conditional likelihood estimation of sparse Ising models (Q693730) (← links)
- Sparse factor regression via penalized maximum likelihood estimation (Q725684) (← links)
- Some theoretical results on the grouped variables Lasso (Q734551) (← links)
- Sparse recovery via differential inclusions (Q739470) (← links)
- Tight conditions for consistency of variable selection in the context of high dimensionality (Q741803) (← links)
- Bayesian adaptive Lasso (Q743993) (← links)
- High-dimensional model recovery from random sketched data by exploring intrinsic sparsity (Q782446) (← links)
- Adaptive and reversed penalty for analysis of high-dimensional correlated data (Q823261) (← links)
- Fitting sparse linear models under the sufficient and necessary condition for model identification (Q826666) (← links)
- Nonsmoothness in machine learning: specific structure, proximal identification, and applications (Q829492) (← links)
- A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model (Q830540) (← links)
- Penalized logspline density estimation using total variation penalty (Q830579) (← links)
- Some sharp performance bounds for least squares regression with \(L_1\) regularization (Q834334) (← links)
- Near-ideal model selection by \(\ell _{1}\) minimization (Q834335) (← links)
- High-dimensional variable selection (Q834336) (← links)
- Properties and refinements of the fused Lasso (Q834368) (← links)
- Sparsity in penalized empirical risk minimization (Q838303) (← links)
- On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding (Q842925) (← links)
- Exact support recovery for sparse spikes deconvolution (Q887157) (← links)
- Optimal variable selection in multi-group sparse discriminant analysis (Q887251) (← links)
- Bayesian linear regression with sparse priors (Q888501) (← links)
- Controlling the false discovery rate via knockoffs (Q888503) (← links)
- Efficient nonconvex sparse group feature selection via continuous and discrete optimization (Q892230) (← links)
- Bridge estimators and the adaptive Lasso under heteroscedasticity (Q893067) (← links)
- Model selection and estimation in high dimensional regression models with group SCAD (Q893964) (← links)
- Learning theory approach to a system identification problem involving atomic norm (Q895425) (← links)
- \(\ell_1\)-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors (Q898600) (← links)
- Online streaming feature selection using rough sets (Q899464) (← links)
- Finite mixture regression: a sparse variable selection by model selection for clustering (Q902208) (← links)
- A note on the Lasso for Gaussian graphical model selection (Q927362) (← links)
- Discussion: One-step sparse estimates in nonconcave penalized likelihood models (Q939651) (← links)
- Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models (Q939653) (← links)
- The sparsity and bias of the LASSO selection in high-dimensional linear regression (Q939654) (← links)
- ``Preconditioning'' for feature selection and regression in high-dimensional problems (Q939656) (← links)
- Shrinkage and model selection with correlated variables via weighted fusion (Q961274) (← links)
- Image denoising via solution paths (Q970161) (← links)
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression (Q973867) (← links)
- Least angle and \(\ell _{1}\) penalized regression: a review (Q975564) (← links)
- High-dimensional Gaussian model selection on a Gaussian design (Q985331) (← links)
- Variable selection in nonparametric additive models (Q988006) (← links)
- SPADES and mixture models (Q988014) (← links)
- Feature selection guided by structural information (Q993274) (← links)
- Lasso-type recovery of sparse representations for high-dimensional data (Q1002157) (← links)
- SCAD-penalized regression in high-dimensional partially linear models (Q1020975) (← links)
- Elastic-net regularization in learning theory (Q1023403) (← links)
- Sparsistency and rates of convergence in large covariance matrix estimation (Q1043730) (← links)
- Estimating high-dimensional intervention effects from observational data (Q1043733) (← links)
- Nonnegative-Lasso and application in index tracking (Q1615217) (← links)
- LOL selection in high dimension (Q1621355) (← links)
