Discussion: The Dantzig selector: Statistical estimation when $p$ is much larger than $n$

Abstract

Discussion of ``The Dantzig selector: Statistical estimation when $p$ is much larger than $n$'' [math/0506081]

Published in at http://dx.doi.org/10.1214/009053607000000442 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (13)

• Rejoinder to "Least angle regression" by Efron et al
• Least Angle Regression
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Nonconcave penalized likelihood with a diverging number of parameters
• Variable selection using MM algorithms
• Sure Independence Screening for Ultra-High Dimensional Feature Space

Cited by in corpus (10)

• Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework
• Continuous-Domain Solutions of Linear Inverse Problems with Tikhonov vs. Generalized TV Regularization
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• The Dantzig selector: Recovery of Signal via ${\ell }_1-\alpha {\ell }_2$ Minimization
• Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries
• Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of large-scale open quantum systems
• Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
• Finding Dantzig selectors with a proximity operator based fixed-point algorithm
• Phase Transition in Limiting Distributions of Coherence of High-Dimensional Random Matrices
• Sparse linear regression -- CLuP achieves the ideal \emph{exact} ML