Comment: Feature Screening and Variable Selection via Iterative Ridge Regression
暂无分享,去创建一个
[1] Jianqing Fan,et al. Nonconcave penalized likelihood with a diverging number of parameters , 2004, math/0406466.
[2] Jianqing Fan,et al. Nonconcave Penalized Likelihood With NP-Dimensionality , 2009, IEEE Transactions on Information Theory.
[3] Jianqing Fan,et al. High Dimensional Classification Using Features Annealed Independence Rules. , 2007, Annals of statistics.
[4] I. Johnstone,et al. Ideal spatial adaptation by wavelet shrinkage , 1994 .
[5] Emmanuel J. Candès,et al. Matrix Completion With Noise , 2009, Proceedings of the IEEE.
[6] W Y Zhang,et al. Discussion on `Sure independence screening for ultra-high dimensional feature space' by Fan, J and Lv, J. , 2008 .
[7] Yuling Yan,et al. Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization , 2019, SIAM J. Optim..
[8] Yuling Yan,et al. Inference and uncertainty quantification for noisy matrix completion , 2019, Proceedings of the National Academy of Sciences.
[9] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[10] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[11] Martin J. Wainwright,et al. Estimation of (near) low-rank matrices with noise and high-dimensional scaling , 2009, ICML.
[12] K. Lange. A gradient algorithm locally equivalent to the EM algorithm , 1995 .
[13] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[14] Po-Ling Loh,et al. Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima , 2013, J. Mach. Learn. Res..
[15] Peter D. Hoff,et al. Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization , 2016, Comput. Stat. Data Anal..
[16] Runze Li,et al. Statistical Foundations of Data Science , 2020 .
[17] Jianqing Fan,et al. Regularization of Wavelet Approximations , 2001 .
[18] Po-Ling Loh,et al. Statistical consistency and asymptotic normality for high-dimensional robust M-estimators , 2015, ArXiv.
[19] V. Koltchinskii,et al. Nuclear norm penalization and optimal rates for noisy low rank matrix completion , 2010, 1011.6256.
[20] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[21] Weijie J. Su,et al. SLOPE-ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION. , 2014, The annals of applied statistics.
[22] D. Hunter,et al. Variable Selection using MM Algorithms. , 2005, Annals of statistics.
[23] A. Tikhonov. On the stability of inverse problems , 1943 .
[24] Po-Ling Loh,et al. Support recovery without incoherence: A case for nonconvex regularization , 2014, ArXiv.
[25] J. Friedman,et al. A Statistical View of Some Chemometrics Regression Tools , 1993 .
[26] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.