Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig

Abstract

Discussion of ``The Dantzig selector: Statistical estimation when $p$ is much larger than $n$'' by Emmanuel Candes and Terence Tao [math/0506081]

Published in at http://dx.doi.org/10.1214/009053607000000460 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Rejoinder to "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
• 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
• High-dimensional graphs and variable selection with the Lasso
• Piecewise linear regularized solution paths
• Lasso-type recovery of sparse representations for high-dimensional data
• High-dimensional generalized linear models and the lasso
• Boosting for high-dimensional linear models
• Sparsity oracle inequalities for the Lasso
• Sharp thresholds for high-dimensional and noisy recovery of sparsity

Cited by in corpus (7)

• Nearly unbiased variable selection under minimax concave penalty
• Lasso-type recovery of sparse representations for high-dimensional data
• A unified approach to model selection and sparse recovery using regularized least squares
• Least angle and ${\ell }_1$ penalized regression: A review
• High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
• The Dantzig selector: Recovery of Signal via ${\ell }_1-\alpha {\ell }_2$ Minimization
• Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of large-scale open quantum systems