Fetching the paper…
Reading the bibliography…
Oracle inequalities and variable selection properties for the Lasso in linear models have been established under a variety of different assumptions on the design matrix.
Extreme eigenvalues of Toeplitz forms and applications to elliptic difference equations
S. Parter · 1961
Earlier work this paper cites.
Introduction to linear optimization
D. Bertsimas and J. Tsitsiklis · 1997
Earlier work this paper cites.
Decoding by linear programming
E. Candès and T. Tao · 2005
Earlier work this paper cites.
High-dimensional graphs and variable selection with the Lasso
N. Meinshausen and P. Bühlmann · 2006
Earlier work this paper cites.
On model selection consistency of Lasso
P. Zhao and B. Yu · 2006
Earlier work this paper cites.
The adaptive Lasso and its oracle properties
H. Zou · 2006
Earlier work this paper cites.
Sparse Density Estimation with ℓ 1 \ell_{1} Penalties
F. Bunea, A.B. Tsybakov, and M.H. Wegkamp · 2007
Cited alongside, same era.
The Dantzig selector: statistical estimation when p is much larger than n
E. Candès and T. Tao · 2007
Cited alongside, same era.
The deterministic Lasso
S. van de Geer · 2007
Cited alongside, same era.
Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
K. Lounici · 2008
Cited alongside, same era.
High-dimensional generalized linear models and the Lasso
S. van de Geer · 2008
Cited alongside, same era.
The sparsity and bias of the Lasso selection in high-dimensional linear regression
C.-H. Zhang and J. Huang · 2008
Cited alongside, same era.
Aggregation for Gaussian regression
Simultaneous analysis of Lasso and Dantzig selector
P. Bickel, Y. Ritov, and A. Tsybakov · 2009
Closest in time.
On recovery of sparse signals via ℓ 1 \ell_{1} minimization
T. Cai, G. Xu, and J. Zhang · 2009
Closest in time.
Near-ideal model selection by ℓ 1 \ell_{1} minimization
E. Candès and Y. Plan · 2009
Closest in time.
Lasso-type recovery of sparse representations for high-dimensional data
N. Meinshausen and B. Yu · 2009
Closest in time.
Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso)
M. Wainwright · 2009
Closest in time.
Some sharp performance bounds for least squares regression with L1 regularization
T. Zhang · 2009
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Bunea, A. Tsybakov, and M. Wegkamp
Cited in the paper.
Sparsity oracle inequalities for the Lasso
F. Bunea, A. Tsybakov, and M. Wegkamp
Cited in the paper.
Shifting inequality and recovery of sparse signals
T. Cai, L. Wang, and G. Xu
Cited in the paper.
Stable recovery of sparse signals and an oracle inequality
T. Cai, L. Wang, and G. Xu
Cited in the paper.
Sparsity in penalized empirical risk minimization
V. Koltchinskii
Cited in the paper.
The Dantzig selector and sparsity oracle inequalities
V. Koltchinskii
Cited in the paper.