Forward regression for Cox models with high-dimensional covariates
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[1] Jianqing Fan,et al. Sure independence screening in generalized linear models with NP-dimensionality , 2009, The Annals of Statistics.
[2] Ronghui Xu,et al. USING PROFILE LIKELIHOOD FOR SEMIPARAMETRIC MODEL SELECTION WITH APPLICATION TO PROPORTIONAL HAZARDS MIXED MODELS. , 2009, Statistica Sinica.
[3] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[4] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[5] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[6] A. Raftery,et al. Bayesian Information Criterion for Censored Survival Models , 2000, Biometrics.
[7] David C Christiani,et al. Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes , 2018, Biometrics.
[8] Ning Hao,et al. Interaction Screening for Ultrahigh-Dimensional Data , 2014, Journal of the American Statistical Association.
[9] Yi Li,et al. Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review , 2017, Applied mathematics : a journal of Chinese universities.
[10] Thomas H. Scheike,et al. Independent screening for single‐index hazard rate models with ultrahigh dimensional features , 2011, 1105.3361.
[11] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[12] Zehua Chen,et al. Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space , 2014 .
[13] Lan Wang,et al. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data , 2013, 1304.2186.
[14] H. Zou. A note on path-based variable selection in the penalized proportional hazards model , 2008 .
[15] L. J. Wei,et al. The Robust Inference for the Cox Proportional Hazards Model , 1989 .
[16] Yichao Wu,et al. Ultrahigh Dimensional Feature Selection: Beyond The Linear Model , 2009, J. Mach. Learn. Res..
[17] Charles E McCulloch,et al. Relaxing the rule of ten events per variable in logistic and Cox regression. , 2007, American journal of epidemiology.
[18] Jinfeng Xu,et al. Extended Bayesian information criterion in the Cox model with a high-dimensional feature space , 2014, Annals of the Institute of Statistical Mathematics.
[19] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[20] Toshio Honda,et al. Forward Variable Selection for Sparse Ultra-High Dimensional Varying Coefficient Models , 2014, 1410.6556.
[21] A. Belloni,et al. L1-Penalised quantile regression in high-dimensional sparse models , 2009 .
[22] Shuangge Ma,et al. Censored Rank Independence Screening for High-dimensional Survival Data. , 2014, Biometrika.
[23] Jason P. Fine,et al. Comparing nonnested Cox models , 2002 .
[24] Wenxuan Zhong,et al. Correlation pursuit: forward stepwise variable selection for index models , 2012, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[25] Cun-Hui Zhang,et al. ORACLE INEQUALITIES FOR THE LASSO IN THE COX MODEL. , 2013, Annals of statistics.
[26] J. Minna,et al. Aberrant DNA methylation in lung cancer: biological and clinical implications. , 2002, The oncologist.
[27] Hansheng Wang. Forward Regression for Ultra-High Dimensional Variable Screening , 2009 .
[28] Jeffrey S. Morris,et al. Sure independence screening for ultrahigh dimensional feature space Discussion , 2008 .
[29] Qi Zheng,et al. Survival impact index and ultrahigh‐dimensional model‐free screening with survival outcomes , 2016, Biometrics.
[30] Yi Li,et al. Conditional screening for ultra-high dimensional covariates with survival outcomes , 2016, Lifetime data analysis.
[31] Qi Zheng,et al. GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA. , 2015, Annals of statistics.
[32] Jiahua Chen,et al. Extended Bayesian information criteria for model selection with large model spaces , 2008 .
[33] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[34] Yi Li,et al. Principled sure independence screening for Cox models with ultra-high-dimensional covariates , 2012, J. Multivar. Anal..
[35] O. Bousquet. A Bennett concentration inequality and its application to suprema of empirical processes , 2002 .
[36] T. Lai,et al. A STEPWISE REGRESSION METHOD AND CONSISTENT MODEL SELECTION FOR HIGH-DIMENSIONAL SPARSE LINEAR MODELS , 2011 .
[37] S. Geer. HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO , 2008, 0804.0703.
[38] Jianqing Fan,et al. REGULARIZATION FOR COX'S PROPORTIONAL HAZARDS MODEL WITH NP-DIMENSIONALITY. , 2010, Annals of statistics.
[39] S. Kong,et al. Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso. , 2012, Statistica Sinica.
[40] M. Talagrand. Sharper Bounds for Gaussian and Empirical Processes , 1994 .