Variable screening for ultrahigh dimensional censored quantile regression

References

• Tibshirani R. Regression shrinkage and selection via the LASSO. J R Stat Soc Ser B. 1996;58:267–288.
   Google Scholar
• Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96:1348–1360. doi: 10.1198/016214501753382273
   Web of Science ®Google Scholar
• Zou H. The Adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101:1418–1429. doi: 10.1198/016214506000000735
   Web of Science ®Google Scholar
• Candes E, Tao T. The dantzig selector: statistical estimation when p is much larger than n (with disscussion). Ann Statist. 2007;35:2313–2404. doi: 10.1214/009053606000001523
   Web of Science ®Google Scholar
• Zhang CH. Nearly unbiased variable selection under minimax concave penalty. Ann Statist. 2010;38:894–942. doi: 10.1214/09-AOS729
   Web of Science ®Google Scholar
• Fan J, Lv J. Sure independence screening for ultra-high dimensional feature space (with discussion). J R Stat Soc Ser B. 2008;70:849–911. doi: 10.1111/j.1467-9868.2008.00674.x
   PubMed Web of Science ®Google Scholar
• Fan J, Song R. Sure independence screening in generalized linear models with NP- dimensionality. Ann Statist. 2010;38:3567–3604. doi: 10.1214/10-AOS798
   Web of Science ®Google Scholar
• Fan J, Feng Y, Song R. Nonparametric independence screening in sparse ultra-high dimensional additive models. J Am Stat Assoc. 2011;106:544–557. doi: 10.1198/jasa.2011.tm09779
   PubMed Web of Science ®Google Scholar
• Zhu LP, Li L, Li R, et al. Model-free feature screening for ultrahigh- dimensional data. J Am Stat Assoc. 2011;106:1464–1475. doi: 10.1198/jasa.2011.tm10563
   PubMed Web of Science ®Google Scholar
• Fan J, Ma Y, Dai W. Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models. J Am Stat Assoc. 2014;109:1270–1284. doi: 10.1080/01621459.2013.879828
   PubMed Web of Science ®Google Scholar
• Liu J, Li R, Wu R. Feature selection for varying coefficient models with ultrahigh dimensional covariates. J Am Stat Assoc. 2014;109:266–274. doi: 10.1080/01621459.2013.850086
   PubMed Web of Science ®Google Scholar
• Li R, Zhong W, Zhu L. Feature screening via distance correlation learning. J Am Stat Assoc. 2012;107:1129–1139. doi: 10.1080/01621459.2012.695654
   PubMed Web of Science ®Google Scholar
• Li G, Peng H, Zhang J, et al. Robust rank correlation based screening. Ann Statist. 2012;40:1846–1877. doi: 10.1214/12-AOS1024
   Web of Science ®Google Scholar
• Shao X, Zhang J. Martingale difference correlation and its use in high-dimensional variable screening. J Am Stat Assoc. 2014;109:1302–1318. doi: 10.1080/01621459.2014.887012
   Web of Science ®Google Scholar
• Zhao DS, Li Y. Principled sure independence screening for Cox models with ultra-high-dimensional covariates. J Multivar Anal. 2012;105:397–411. doi: 10.1016/j.jmva.2011.08.002
   PubMed Web of Science ®Google Scholar
• Gorst-Rasmussen A, Scheike T. Independent screening for single-index hazard rate models with ultrahigh dimensional features. J R Stat Soc Ser B. 2013;75:217–245. doi: 10.1111/j.1467-9868.2012.01039.x
   Google Scholar
• Song R, Lu W, Ma S, et al. Censored rank independence screening for high-dimensional survival data. Biometrika. 2014;101:799–814. doi: 10.1093/biomet/asu047
   PubMed Web of Science ®Google Scholar
• Zhou T, Zhu L. Model-free feature screening for ultrahigh dimensional censored regression. Stat Comput. 2016;0:1–15.
   Google Scholar
• He X, Wang L, Hong HG. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data. Ann Statist. 2013;41:342–369. doi: 10.1214/13-AOS1087
   Web of Science ®Google Scholar
• Wu Y, Yin G. Conditional quantile screening in ultrahigh-dimensional heterogeneous data. Biometrika. 2015;102:65–76. doi: 10.1093/biomet/asu068
   Web of Science ®Google Scholar
• Wang H. Forward regression for ultra-high dimensional variable screening. J Am Stat Assoc. 2009;104:1512–1524. doi: 10.1198/jasa.2008.tm08516
   Web of Science ®Google Scholar
• Ma S, Li R, Tsai CL. Variable screening via quantile partial correlation. J Am Stat Assoc. 2017;112:650–663. doi: 10.1080/01621459.2016.1156545
   PubMed Web of Science ®Google Scholar
• Li G, Li Y, Tsai CL. Quantile correlations and quantile autoregressive modeling. J Am Stat Assoc. 2015;110:246–261. doi: 10.1080/01621459.2014.892007
   Web of Science ®Google Scholar
• Beran R. Nonparametric regression with randomly censored survival data [Technical report]. Berkeley: University of California; 1981.
   Google Scholar
• Dabrowska DM. Uniform consistency of kernel conditional Kaplan-Meier estimate. Ann Statist. 1989;17:1157–1167. doi: 10.1214/aos/1176347261
   Web of Science ®Google Scholar
• Gonzalez-Manteiga W, Cadarso-Suarez C. Asymptotic properties of a generalized Kaplan-Meier estimator with some applications. J Nonparametr Stat. 1994;4:65–78. doi: 10.1080/10485259408832601
   Google Scholar
• Sheather SJ, Jones MC. A reliable data-based bandwidth selection method for kernel density estimation. J R Stat Soc Ser B. 1991;53:683–690.
   Google Scholar
• Wang L, Wu Y, Li R. Quantile regression for analyzing heterogeneity in ultra-high dimension. J Am Stat Assoc. 2012;107:214–222. doi: 10.1080/01621459.2012.656014
   PubMed Web of Science ®Google Scholar
• Lee ER, Noh H, Park BU. Model selection via Bayesian Information Criterion for quantile regression models. J Am Stat Assoc. 2014;109:216–229. doi: 10.1080/01621459.2013.836975
   Web of Science ®Google Scholar
• Fan J, Gijbels I. Local polynomial modeling and its applications. New York (NY): Chapman and Hall; 1996.
   Google Scholar
• Peng L, Huang Y. Survival analysis with quantile regression models. J Am Stat Assoc. 2008;103:637–649. doi: 10.1198/016214508000000355
   Web of Science ®Google Scholar
• Portnoy S. Censored regression quantiles. J Am Stat Assoc. 2003;98:1001–1012. doi: 10.1198/016214503000000954
   Web of Science ®Google Scholar
• Wang HJ, Wang L. Locally weighted censored quantile regression. J Am Stat Assoc. 2009;104:1117–1128. doi: 10.1198/jasa.2009.tm08230
   Web of Science ®Google Scholar
• Cui H, Li R, Zhong W. Model-free feature screening for ultrahigh dimensional discriminant analysis. J Am Stat Assoc. 2015;110:630–641. doi: 10.1080/01621459.2014.920256
   PubMed Web of Science ®Google Scholar
• Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling topredict survival after chemotherapy for diffuse large-Bcell lymphoma. N Engl J Med. 2002;346:1937–1947. doi: 10.1056/NEJMoa012914
   PubMed Web of Science ®Google Scholar
• Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2004;2:511–522. doi: 10.1371/journal.pbio.0020108
   Web of Science ®Google Scholar
• Wang HJ, Zhou J, Li Y. Variable selection for censored quantile regresion. Stat Sin. 2013;23:145–167.
   PubMed Web of Science ®Google Scholar
• Harrell FE, Davis CE. A new distribution-free quantile estimator. Biometrika. 1982;69:635–640. doi: 10.1093/biomet/69.3.635
   Web of Science ®Google Scholar
• Pencina MJ, D'Agostino RB. Overall c as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23:2109–2123. doi: 10.1002/sim.1802
   PubMed Web of Science ®Google Scholar