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-AOS0316A the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Discussion of "Least angle regression" by Efron et al
• 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
• High-dimensional graphs and variable selection with the Lasso
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• The sparsity and bias of the Lasso selection in high-dimensional linear regression
• Lasso-type recovery of sparse representations for high-dimensional data
• High-dimensional generalized linear models and the lasso

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• Variable selection in nonparametric additive models
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• An adaptive Ridge procedure for L0 regularization
• Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
• Partial Correlation Graphical LASSO
• Non-bifurcating phylogenetic tree inference via the adaptive LASSO
• An Algorithm for Nonlinear, Nonparametric Model Choice and Prediction
• Fixed effects selection in the linear mixed-effects model using adaptive ridge procedure for L0 penalty performance
• On an improvement of LASSO by scaling
• Sparse estimation for case-control studies with multiple subtypes of cases
• A Weibull Mixture Cure Frailty Model for High-dimensional Covariates
• On the use of cross-validation for the calibration of the adaptive lasso
• MSP: A Multi-step Screening Procedure for Sparse Recovery