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

Abstract

Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]

Published in at http://dx.doi.org/10.1214/07-AOS0316C the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (5)

• High-dimensional graphs and variable selection with the Lasso
• Nonconcave penalized likelihood with a diverging number of parameters
• The sparsity and bias of the Lasso selection in high-dimensional linear regression
• Lasso-type recovery of sparse representations for high-dimensional data
• Sharp thresholds for high-dimensional and noisy recovery of sparsity

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• Nearly unbiased variable selection under minimax concave penalty
• MM Algorithms for Minimizing Nonsmoothly Penalized Objective Functions
• Sparse Variable Selection on High Dimensional Heterogeneous Data with Tree Structured Responses
• A Laplace Mixture Representation of the Horseshoe and Some Implications
• On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series
• Learning Multi-Attribute Differential Graphs with Non-Convex Penalties
• Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties