Pages that link to "Item:Q147375"

The following pages link to The Adaptive Lasso and Its Oracle Properties (Q147375):

Displaying 50 items.

• Tests for differential Gaussian Bayesian networks based on quadratic inference functions (Q830113) (← links)
• Variable selection in multivariate linear models for functional data via sparse regularization (Q830251) (← links)
• Penalized least squares approximation methods and their applications to stochastic processes (Q830256) (← links)
• Variable selection for generalized odds rate mixture cure models with interval-censored failure time data (Q830426) (← links)
• Model-free variable selection for conditional mean in regression (Q830544) (← links)
• Model detection and estimation for varying coefficient panel data models with fixed effects (Q830568) (← links)
• Group orthogonal greedy algorithm for change-point estimation of multivariate time series (Q830674) (← links)
• A new variant of the parallel regression model with variable selection in surveys with sensitive attribute (Q830681) (← links)
• High-dimensional variable selection (Q834336) (← links)
• On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding (Q842925) (← links)
• Automatic model selection for partially linear models (Q842929) (← links)
• Least squares approximation with a diverging number of parameters (Q844883) (← links)
• Functional index coefficient models with variable selection (Q888320) (← links)
• Estimation and inference in generalized additive coefficient models for nonlinear interactions with high-dimensional covariates (Q888506) (← links)
• Globally adaptive quantile regression with ultra-high dimensional data (Q888510) (← links)
• Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms (Q889303) (← links)
• Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm (Q890292) (← links)
• Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models (Q892254) (← links)
• Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation (Q892458) (← links)
• Robust direction identification and variable selection in high dimensional general single-index models (Q892888) (← links)
• Bridge estimators and the adaptive Lasso under heteroscedasticity (Q893067) (← links)
• The quantile process under random censoring (Q893069) (← links)
• Model selection and estimation in high dimensional regression models with group SCAD (Q893964) (← links)
• Sparse identification of posynomial models (Q894327) (← links)
• Shrinkage estimation of dynamic panel data models with interactive fixed effects (Q894645) (← links)
• Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting (Q894843) (← links)
• Efficient estimation of approximate factor models via penalized maximum likelihood (Q898581) (← links)
• Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (Q898588) (← links)
• \(\ell_1\)-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors (Q898600) (← links)
• Incorporating grouping information in Bayesian variable selection with applications in genomics (Q899018) (← links)
• \(k\)-sample upper expectation linear regression-modeling, identifiability, estimation and prediction (Q899350) (← links)
• Dimension reduction based linear surrogate variable approach for model free variable selection (Q900762) (← links)
• Separation of linear and index covariates in partially linear single-index models (Q900792) (← links)
• Practical variable selection for generalized additive models (Q901636) (← links)
• SLOPE-adaptive variable selection via convex optimization (Q902886) (← links)
• Simultaneous estimation and variable selection in median regression using Lasso-type penalty (Q904101) (← links)
• Composite quantile regression and the oracle model selection theory (Q930648) (← 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)
• Iterative thresholding algorithms (Q942154) (← links)
• Shrinkage and model selection with correlated variables via weighted fusion (Q961274) (← links)
• A sparse eigen-decomposition estimation in semiparametric regression (Q962349) (← links)
• Sparse estimation and inference for censored median regression (Q963882) (← links)
• Image denoising via solution paths (Q970161) (← links)
• On sparse estimation for semiparametric linear transformation models (Q972891) (← links)
• Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images (Q975156) (← links)
• Least angle and \(\ell _{1}\) penalized regression: a review (Q975564) (← links)
• Variable selection for semiparametric varying coefficient partially linear errors-in-variables models (Q979240) (← links)