BOOTSTRAP-BASED PENALTY CHOICE FOR THE LASSO , ACHIEVING ORACLE PERFORMANCE
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[1] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[2] Joel A. Tropp,et al. Recovery of short, complex linear combinations via /spl lscr//sub 1/ minimization , 2005, IEEE Transactions on Information Theory.
[3] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[4] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[5] L. Breiman. Better subset regression using the nonnegative garrote , 1995 .
[6] V. V. Petrov. Sums of Independent Random Variables , 1975 .
[7] Michael C. Ferris,et al. Model building with likelihood basis pursuit , 2004, Optim. Methods Softw..
[8] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[9] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[10] D. Donoho. For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .
[11] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[12] Hong-Ye Gao,et al. Wavelet Shrinkage Denoising Using the Non-Negative Garrote , 1998 .
[13] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[14] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[15] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[16] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .