Conditional Sure Independence Screening
暂无分享,去创建一个
[1] J. Friedman,et al. [A Statistical View of Some Chemometrics Regression Tools]: Response , 1993 .
[2] E. Fama,et al. Common risk factors in the returns on stocks and bonds , 1993 .
[3] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[4] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[5] M. R. Osborne,et al. On the LASSO and its Dual , 2000 .
[6] M. R. Osborne,et al. A new approach to variable selection in least squares problems , 2000 .
[7] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[8] J. Dongen,et al. Comparative analysis of T-cell receptor gene rearrangements at diagnosis and relapse of T-cell acute lymphoblastic leukemia (T-ALL) shows high stability of clonal markers for monitoring of minimal residual disease and reveals the occurrence of second T-ALL , 2003, Leukemia.
[9] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[10] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[11] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[12] C. Robert. Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv. , 2008 .
[13] Zhanfeng Wang,et al. Asymptotic Normality of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Fixed Design* , 2008, J. Syst. Sci. Complex..
[14] Yichao Wu,et al. Ultrahigh Dimensional Feature Selection: Beyond The Linear Model , 2009, J. Mach. Learn. Res..
[15] L. Wasserman,et al. HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.
[16] P. Hall,et al. Tilting methods for assessing the influence of components in a classifier , 2009 .
[17] Hansheng Wang. Forward Regression for Ultra-High Dimensional Variable Screening , 2009 .
[18] Peter Hall,et al. Using Generalized Correlation to Effect Variable Selection in Very High Dimensional Problems , 2009 .
[19] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[20] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[21] Jianqing Fan,et al. Sure independence screening in generalized linear models with NP-dimensionality , 2009, The Annals of Statistics.
[22] Yang Feng,et al. Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models , 2009, Journal of the American Statistical Association.
[23] Runze Li,et al. Model-Free Feature Screening for Ultrahigh-Dimensional Data , 2011, Journal of the American Statistical Association.
[24] Sara van de Geer,et al. Statistics for High-Dimensional Data , 2011 .
[25] Jianqing Fan,et al. Nonconcave Penalized Likelihood With NP-Dimensionality , 2009, IEEE Transactions on Information Theory.
[26] Tong Zhang,et al. A General Theory of Concave Regularization for High-Dimensional Sparse Estimation Problems , 2011, 1108.4988.
[27] Jun Zhang,et al. Robust rank correlation based screening , 2010, 1012.4255.
[28] Yi Li,et al. Principled sure independence screening for Cox models with ultra-high-dimensional covariates , 2012, J. Multivar. Anal..
[29] Runze Li,et al. Feature Screening via Distance Correlation Learning , 2012, Journal of the American Statistical Association.
[30] Hengjian Cui,et al. Model-Free Feature Screening for Ultrahigh , 2014 .
[31] 秀俊 松井,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2014 .
[32] Ulrich Amsel,et al. Quasi Likelihood And Its Application A General Approach To Optimal Parameter Estimation , 2016 .
[33] Jianqing Fan,et al. Sure Independence Screening , 2018 .