Forward Regression for Ultra-High Dimensional Variable Screening
暂无分享,去创建一个
[1] P. McCullagh,et al. Generalized Linear Models , 1992 .
[2] L. Breiman. Better subset regression using the nonnegative garrote , 1995 .
[3] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[4] Wenjiang J. Fu. Penalized Regressions: The Bridge versus the Lasso , 1998 .
[5] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[6] Eric R. Ziegel,et al. Generalized Linear Models , 2002, Technometrics.
[7] H. Zou,et al. Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays , 2003 .
[8] Jianqing Fan,et al. Nonconcave penalized likelihood with a diverging number of parameters , 2004, math/0406466.
[9] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[10] D. Hunter,et al. Variable Selection using MM Algorithms. , 2005, Annals of statistics.
[11] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[12] M. Yuan,et al. On the Nonnegative Garrote Estimator , 2005 .
[13] Runze Li,et al. Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery , 2006, math/0602133.
[14] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[15] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[16] Victoria Stodden,et al. Breakdown Point of Model Selection When the Number of Variables Exceeds the Number of Observations , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[17] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[18] G. Wahba,et al. A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTION , 2006 .
[19] M. Yuan,et al. On the non‐negative garrotte estimator , 2007 .
[20] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[21] Hao Helen Zhang,et al. Adaptive Lasso for Cox's proportional hazards model , 2007 .
[22] Yingcun Xia,et al. Variable selection for the single‐index model , 2007 .
[23] H. Zou,et al. One-step Sparse Estimates in Nonconcave Penalized Likelihood Models. , 2008, Annals of statistics.
[24] Bin Yu,et al. On Model Selection Consistency of the Elastic Net When p >> n , 2008 .
[25] C. Robert. Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv. , 2008 .
[26] P. Bickel,et al. Regularized estimation of large covariance matrices , 2008, 0803.1909.
[27] Cun-Hui Zhang,et al. Adaptive Lasso for sparse high-dimensional regression models , 2008 .
[28] A. Barron,et al. Approximation and learning by greedy algorithms , 2008, 0803.1718.
[29] Jeffrey S. Morris,et al. Sure independence screening for ultrahigh dimensional feature space Discussion , 2008 .
[30] Jiahua Chen,et al. Extended Bayesian information criteria for model selection with large model spaces , 2008 .
[31] H. Zou,et al. One-step Sparse Estimates in Nonconcave Penalized Likelihood Models. , 2008, Annals of statistics.
[32] Cun-Hui Zhang,et al. The sparsity and bias of the Lasso selection in high-dimensional linear regression , 2008, 0808.0967.
[33] J. Horowitz,et al. Asymptotic properties of bridge estimators in sparse high-dimensional regression models , 2008, 0804.0693.
[34] Hao Helen Zhang,et al. ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. , 2009, Annals of statistics.