Pages that link to "Item:Q939654"

The following pages link to The sparsity and bias of the LASSO selection in high-dimensional linear regression (Q939654):

Displaying 50 items.

• High dimensional censored quantile regression (Q1747740) (← links)
• Bayesian estimation of sparse signals with a continuous spike-and-slab prior (Q1747745) (← links)
• Identification of breast cancer prognosis markers via integrative analysis (Q1927047) (← links)
• An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors (Q1927082) (← links)
• \(\ell _{1}\)-regularized linear regression: persistence and oracle inequalities (Q1930861) (← links)
• Needles and straw in a haystack: posterior concentration for possibly sparse sequences (Q1940767) (← links)
• Lasso, iterative feature selection and the correlation selector: oracle inequalities and numerical performances (Q1951793) (← links)
• Thresholding-based iterative selection procedures for model selection and shrinkage (Q1951984) (← links)
• On the conditions used to prove oracle results for the Lasso (Q1952029) (← links)
• Sparse regression with exact clustering (Q1952092) (← links)
• Adaptive estimation of covariance matrices via Cholesky decomposition (Q1952094) (← links)
• The Lasso as an \(\ell _{1}\)-ball model selection procedure (Q1952205) (← links)
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (Q1952206) (← links)
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods (Q1952223) (← links)
• Least squares after model selection in high-dimensional sparse models (Q1952433) (← links)
• Sign-constrained least squares estimation for high-dimensional regression (Q1954143) (← links)
• Debiasing the Lasso: optimal sample size for Gaussian designs (Q1991670) (← links)
• Adaptive group Lasso for high-dimensional generalized linear models (Q2010806) (← links)
• Learning latent variable Gaussian graphical model for biomolecular network with low sample complexity (Q2011725) (← links)
• Parametric and semiparametric reduced-rank regression with flexible sparsity (Q2018603) (← links)
• Greedy variance estimation for the LASSO (Q2019914) (← links)
• A unified primal dual active set algorithm for nonconvex sparse recovery (Q2038299) (← links)
• Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions (Q2039788) (← links)
• Optimal sparsity testing in linear regression model (Q2040034) (← links)
• Second-order Stein: SURE for SURE and other applications in high-dimensional inference (Q2054467) (← links)
• Optimal linear discriminators for the discrete choice model in growing dimensions (Q2073710) (← links)
• In defense of the indefensible: a very naïve approach to high-dimensional inference (Q2075709) (← links)
• Confidence intervals for parameters in high-dimensional sparse vector autoregression (Q2076143) (← links)
• Feature selection for data integration with mixed multiview data (Q2078739) (← links)
• High-dimensional linear regression with hard thresholding regularization: theory and algorithm (Q2097492) (← links)
• High-dimensional variable screening through kernel-based conditional mean dependence (Q2112254) (← links)
• \(\ell_0\)-regularized high-dimensional accelerated failure time model (Q2129574) (← links)
• Inference for low-rank tensors -- no need to debias (Q2131273) (← links)
• Adaptive log-density estimation (Q2131904) (← links)
• De-biasing the Lasso with degrees-of-freedom adjustment (Q2136990) (← links)
• Bayesian factor-adjusted sparse regression (Q2155305) (← links)
• Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors (Q2172011) (← links)
• Double-slicing assisted sufficient dimension reduction for high-dimensional censored data (Q2215728) (← links)
• Which bridge estimator is the best for variable selection? (Q2215760) (← links)
• A general framework for Bayes structured linear models (Q2215762) (← links)
• Pivotal estimation via square-root lasso in nonparametric regression (Q2249850) (← links)
• High-dimensional variable screening and bias in subsequent inference, with an empirical comparison (Q2259726) (← links)
• Consistency of Bayesian linear model selection with a growing number of parameters (Q2276179) (← links)
• Sorted concave penalized regression (Q2284364) (← links)
• Adaptive group bridge selection in the semiparametric accelerated failure time model (Q2293393) (← links)
• High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking (Q2302521) (← links)
• Localized Gaussian width of \(M\)-convex hulls with applications to Lasso and convex aggregation (Q2325349) (← links)
• A knockoff filter for high-dimensional selective inference (Q2328050) (← links)
• Selection of sparse vine copulas in high dimensions with the Lasso (Q2329765) (← links)
• Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming (Q2330643) (← links)