High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
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[1] Martin J. Wainwright,et al. Fast global convergence of gradient methods for high-dimensional statistical recovery , 2011, ArXiv.
[2] Hyokyoung G Hong,et al. Weak signals in high-dimension regression: detection, estimation and prediction. , 2019, Applied stochastic models in business and industry.
[3] W Leisenring,et al. A marginal regression modelling framework for evaluating medical diagnostic tests. , 1997, Statistics in medicine.
[4] J. Robins,et al. Uniform consistency in causal inference , 2003 .
[5] Yao Wang,et al. Model selection and estimation in high dimensional regression models with group SCAD , 2015 .
[6] Kengo Kato,et al. Gaussian approximation of suprema of empirical processes , 2012, 1212.6885.
[7] Sundeep Rangan,et al. Necessary and Sufficient Conditions for Sparsity Pattern Recovery , 2008, IEEE Transactions on Information Theory.
[8] Shihao Wu,et al. On the early solution path of best subset selection , 2021 .
[9] Yi Li,et al. Conditional screening for ultra-high dimensional covariates with survival outcomes , 2016, Lifetime data analysis.
[10] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[11] Martin J. Wainwright,et al. Lower bounds on the performance of polynomial-time algorithms for sparse linear regression , 2014, COLT.
[12] Wenbin Lu. Marginal Regression of Multivariate Event Times Based on Linear Transformation Models , 2005, Lifetime data analysis.
[13] Qi Zhang,et al. Optimality of graphlet screening in high dimensional variable selection , 2012, J. Mach. Learn. Res..
[14] Runze Li,et al. Feature Screening for Ultrahigh Dimensional Categorical Data With Applications , 2013, Journal of business & economic statistics : a publication of the American Statistical Association.
[15] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[16] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[17] Jianqing Fan,et al. ARE DISCOVERIES SPURIOUS? DISTRIBUTIONS OF MAXIMUM SPURIOUS CORRELATIONS AND THEIR APPLICATIONS. , 2015, Annals of statistics.
[18] Martin J. Wainwright,et al. Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting , 2009, IEEE Trans. Inf. Theory.
[19] L. Wasserman,et al. HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.
[20] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[21] Larry A. Wasserman,et al. A Comparison of the Lasso and Marginal Regression , 2012, J. Mach. Learn. Res..
[22] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[23] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[24] E. Candès,et al. Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism , 2010, 1007.1434.
[25] Bart P. G. Van Parys,et al. Sparse high-dimensional regression: Exact scalable algorithms and phase transitions , 2017, The Annals of Statistics.
[26] Haoyang Liu,et al. Between hard and soft thresholding: optimal iterative thresholding algorithms , 2018, Information and Inference: A Journal of the IMA.
[27] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[28] Martin J. Wainwright,et al. Information-Theoretic Limits on Sparse Signal Recovery: Dense versus Sparse Measurement Matrices , 2008, IEEE Transactions on Information Theory.
[29] Min Chen,et al. Asset selection based on high frequency Sharpe ratio , 2020 .
[30] Mario Bertero,et al. The Stability of Inverse Problems , 1980 .
[31] Jin Zhu,et al. A polynomial algorithm for best-subset selection problem , 2020, Proceedings of the National Academy of Sciences.
[32] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[33] Tzu-Jung Huang,et al. Marginal screening for high-dimensional predictors of survival outcomes. , 2019, Statistica Sinica.
[34] Jukka Corander,et al. Genome-wide association studies with high-dimensional phenotypes , 2012, Statistical applications in genetics and molecular biology.
[35] Prateek Jain,et al. On Iterative Hard Thresholding Methods for High-dimensional M-Estimation , 2014, NIPS.
[36] Alexandre B. Tsybakov,et al. Optimal Variable Selection and Adaptive Noisy Compressed Sensing , 2018, IEEE Transactions on Information Theory.
[37] Runze Li,et al. Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning , 2020, Bioinform..
[38] Simon C. Potter,et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.
[39] Jiashun Jin,et al. UPS delivers optimal phase diagram in high-dimensional variable selection , 2010, 1010.5028.
[40] D. Bertsimas,et al. Best Subset Selection via a Modern Optimization Lens , 2015, 1507.03133.
[41] Jianqing Fan,et al. When is best subset selection the "best"? , 2020 .