Rejoinder: The Dantzig selector: Statistical estimation when is much larger than
arXiv:0803.3136 · doi:10.1214/009053607000000532
Abstract
Rejoinder to ``The Dantzig selector: Statistical estimation when is much larger than '' [math/0506081]
Published in at http://dx.doi.org/10.1214/009053607000000532 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (14)
- 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
- Sparsity and Incoherence in Compressive Sampling
- Lasso-type recovery of sparse representations for high-dimensional data
- Sparsity oracle inequalities for the Lasso
- Aggregation for Gaussian regression
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- Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information
- Comparison of echo state network output layer classification methods on noisy data
- Power-Constrained Sparse Gaussian Linear Dimensionality Reduction over Noisy Channels
- Estimation of dynamic networks for high-dimensional nonstationary time series
- Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of large-scale open quantum systems