The Lasso under Heteroscedasticity
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Bin Yu | Karl Rohe | Jinzhu Jia | Bin Yu | Jinzhu Jia | Karl Rohe
[1] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[2] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[3] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[4] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[5] Michael I. Jordan,et al. Union support recovery in high-dimensional multivariate regression , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.
[6] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[7] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[8] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[9] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[10] M. R. Osborne,et al. On the LASSO and its Dual , 2000 .
[11] Peng Zhao,et al. Stagewise Lasso , 2007, J. Mach. Learn. Res..
[12] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[13] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[14] J. Fessler. Statistical Image Reconstruction Methods for Transmission Tomography , 2000 .
[15] Michael Elad,et al. Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.
[16] S. Chatterjee. An error bound in the Sudakov-Fernique inequality , 2005, math/0510424.
[17] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[18] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[19] S. Szarek,et al. Chapter 8 - Local Operator Theory, Random Matrices and Banach Spaces , 2001 .
[20] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[21] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[22] Saharon Rosset,et al. Tracking Curved Regularized Optimization Solution Paths , 2004, NIPS 2004.
[23] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[24] L. Shepp,et al. A Statistical Model for Positron Emission Tomography , 1985 .
[25] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[26] Martin J. Wainwright,et al. Sharp thresholds for high-dimensional and noisy recovery of sparsity , 2006, ArXiv.
[27] Arkadi Nemirovski,et al. On sparse representation in pairs of bases , 2003, IEEE Trans. Inf. Theory.
[28] Jean-Jacques Fuchs,et al. Recovery of exact sparse representations in the presence of bounded noise , 2005, IEEE Transactions on Information Theory.
[29] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[30] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .
[31] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.