Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low
暂无分享,去创建一个
[1] G. Pisier. Remarques sur un résultat non publié de B. Maurey , 1981 .
[2] T. J. Mitchell,et al. Bayesian Variable Selection in Linear Regression , 1988 .
[3] George L. Nemhauser,et al. Constraint classification for mixed integer programming formulations , 1991 .
[4] A. Atkinson. Subset Selection in Regression , 1992 .
[5] C. Stein,et al. Estimation with Quadratic Loss , 1992 .
[6] T. Hastie,et al. [A Statistical View of Some Chemometrics Regression Tools]: Discussion , 1993 .
[7] Yves Crama,et al. Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.
[8] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[9] S. Thomas McCormick,et al. Integer Programming and Combinatorial Optimization , 1996, Lecture Notes in Computer Science.
[10] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[11] L. Breiman. Heuristics of instability and stabilization in model selection , 1996 .
[12] Pierre Hansen,et al. Variable Neighborhood Search , 2018, Handbook of Heuristics.
[13] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[14] M. Ledoux. The concentration of measure phenomenon , 2001 .
[15] Y. Ritov,et al. Persistence in high-dimensional linear predictor selection and the virtue of overparametrization , 2004 .
[16] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[17] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[18] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[19] Dimitris Bertsimas,et al. Optimization over integers , 2005 .
[20] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[21] Martin J. Wainwright,et al. Sharp thresholds for high-dimensional and noisy recovery of sparsity , 2006, ArXiv.
[22] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[23] E. Greenshtein. Best subset selection, persistence in high-dimensional statistical learning and optimization under l1 constraint , 2006, math/0702684.
[24] Yufeng Liu,et al. Variable Selection via A Combination of the L0 and L1 Penalties , 2007 .
[25] P. Massart,et al. Concentration inequalities and model selection , 2007 .
[26] Nicolai Meinshausen,et al. Relaxed Lasso , 2007, Comput. Stat. Data Anal..
[27] A. Belloni,et al. Least Squares After Model Selection in High-Dimensional Sparse Models , 2009, 1001.0188.
[28] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[29] Hao Helen Zhang,et al. ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. , 2009, Annals of statistics.
[30] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[31] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[32] V. Koltchinskii,et al. Nuclear norm penalization and optimal rates for noisy low rank matrix completion , 2010, 1011.6256.
[33] James G. Scott,et al. The horseshoe estimator for sparse signals , 2010 .
[34] Jeff T. Linderoth,et al. MILP Software , 2010 .
[35] A. Tsybakov,et al. Exponential Screening and optimal rates of sparse estimation , 2010, 1003.2654.
[36] N. Verzelen. Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons , 2010, 1008.0526.
[37] S. Geer,et al. Oracle Inequalities and Optimal Inference under Group Sparsity , 2010, 1007.1771.
[38] Martin J. Wainwright,et al. Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$ -Balls , 2009, IEEE Transactions on Information Theory.
[39] A. Belloni,et al. Pivotal estimation via square-root Lasso in nonparametric regression , 2011, 1105.1475.
[40] Sara van de Geer,et al. Statistics for High-Dimensional Data , 2011 .
[41] Charles Soussen,et al. From Bernoulli–Gaussian Deconvolution to Sparse Signal Restoration , 2011, IEEE Transactions on Signal Processing.
[42] Tong Zhang,et al. A General Theory of Concave Regularization for High-Dimensional Sparse Estimation Problems , 2011, 1108.4988.
[43] Cun-Hui Zhang,et al. Scaled sparse linear regression , 2011, 1104.4595.
[44] Sara van de Geer,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .
[45] A. Dalalyan,et al. Tight conditions for consistency of variable selection in the context of high dimensionality , 2011, 1106.4293.
[46] P. Bartlett,et al. ℓ1-regularized linear regression: persistence and oracle inequalities , 2012 .
[47] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[48] Michael Unser,et al. The analog formulation of sparsity implies infinite divisibility and rules out Bernoulli-Gaussian priors , 2012, 2012 IEEE Information Theory Workshop.
[49] Xingye Qiao,et al. Regularization after retention in ultrahigh dimensional linear regression models , 2013, 1311.5625.
[50] Glenn Stone. Statistics for High‐Dimensional Data: Methods, Theory and Applications. By Peter Buhlmann and Sara van de Geer. Springer, Berlin, Heidelberg. 2011. xvii+556 pages. €104.99 (hardback). ISBN 978‐3‐642‐20191‐2. , 2013 .
[51] Yurii Nesterov,et al. Gradient methods for minimizing composite functions , 2012, Mathematical Programming.
[52] S. Mendelson,et al. Compressed sensing under weak moment assumptions , 2014, 1401.2188.
[53] Jinchi Lv,et al. Asymptotic properties for combined L1 and concave regularization , 2014, 1605.03335.
[54] Ryan J. Tibshirani,et al. Degrees of freedom and model search , 2014, 1402.1920.
[55] A. Dalalyan,et al. On the Prediction Performance of the Lasso , 2014, 1402.1700.
[56] Trevor J. Hastie,et al. Matrix completion and low-rank SVD via fast alternating least squares , 2014, J. Mach. Learn. Res..
[57] Emmanuel J. Candès,et al. False Discoveries Occur Early on the Lasso Path , 2015, ArXiv.
[58] D. Bertsimas,et al. Best Subset Selection via a Modern Optimization Lens , 2015, 1507.03133.
[59] Michael I. Jordan,et al. Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators , 2015, 1503.03188.
[60] Jian Huang,et al. THE Mnet method for variable selection , 2016 .
[61] Rahul Mazumder,et al. The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization , 2015, IEEE Transactions on Information Theory.
[62] R. Tibshirani,et al. Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso , 2017, 1707.08692.
[63] Iain Dunning,et al. Extended formulations in mixed integer conic quadratic programming , 2015, Mathematical Programming Computation.
[64] Stephen G. Walker,et al. Empirical Bayes posterior concentration in sparse high-dimensional linear models , 2014, 1406.7718.
[65] David Gamarnik,et al. High Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transtition , 2017, COLT.
[66] E. George,et al. The Spike-and-Slab LASSO , 2018 .
[67] A. Tsybakov,et al. Slope meets Lasso: Improved oracle bounds and optimality , 2016, The Annals of Statistics.
[68] Lei Sun,et al. Bayesian l 0 ‐regularized least squares , 2017, Applied Stochastic Models in Business and Industry.
[69] Nicholas G. Polson,et al. Prediction Risk for the Horseshoe Regression , 2016, J. Mach. Learn. Res..
[70] Alper Atamtürk,et al. Rank-one Convexification for Sparse Regression , 2019, ArXiv.