Discussion: One-step sparse estimates in nonconcave penalized likelihood models

DOI10.1214/07-AOS0316CzbMath1282.62110arXiv0808.1025OpenAlexW2086621676MaRDI QIDQ5966368

Cun-Hui Zhang

Publication date: 28 August 2008

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0808.1025

Mathematics Subject Classification ID

Related Items (5)

A Unified View of Exact Continuous Penalties for $\ell_2$-$\ell_0$ Minimization ⋮ Majorization-minimization algorithms for nonsmoothly penalized objective functions ⋮ Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator ⋮ Nearly unbiased variable selection under minimax concave penalty ⋮ Fast selection of nonlinear mixed effect models using penalized likelihood

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Lasso-type recovery of sparse representations for high-dimensional data
• Nonconcave penalized likelihood with a diverging number of parameters.
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• High-dimensional graphs and variable selection with the Lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Statistical View of Some Chemometrics Regression Tools
• For most large underdetermined systems of equations, the minimal 𝓁₁‐norm near‐solution approximates the sparsest near‐solution

This page was built for publication: Discussion: One-step sparse estimates in nonconcave penalized likelihood models