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Ultrahigh dimensional feature screening for additive model with multivariate response

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References

  • Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Stat Methodol). 1996;58:267–288.
  • Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96(456):1348–1360. doi: 10.1198/016214501753382273
  • Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Stat Methodol). 2005;67(2):301–320. doi: 10.1111/j.1467-9868.2005.00503.x
  • Candes E, Tao T. The Dantzig selector: statistical estimation when p is much larger than n. Ann Stat. 2007;35:2313–2351. doi: 10.1214/009053606000001523
  • Zhang CH. Nearly unbiased variable selection under minimax concave penalty. Ann Stat. 2010;38(2):894–942. doi: 10.1214/09-AOS729
  • Fan J, Lv J. Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Ser B (Stat Methodol). 2008;70(5):849–911. doi: 10.1111/j.1467-9868.2008.00674.x
  • Fan J, Samworth R, Wu Y. Ultrahigh dimensional feature selection: beyond the linear model. J Mach Learn Res. 2009;10:2013–2038.
  • Fan J, Song R. Sure independence screening in generalized linear models with NP-dimensionality. Ann Stat. 2010;38(6):3567–3604. doi: 10.1214/10-AOS798
  • Hall P, Miller H. Using generalized correlation to effect variable selection in very high dimensional problems. J Comput Graph Stat. 2009;18(3):533–550. doi: 10.1198/jcgs.2009.08041
  • Fan J, Feng Y, Song R. Nonparametric independence screening in sparse ultra-high-dimensional additive models. J Am Stat Assoc. 2011;106(494):544–557. doi: 10.1198/jasa.2011.tm09779
  • Huang J, Horowitz JL, Wei F. Variable selection in nonparametric additive models. Ann Stat. 2010;38(4):2282–2313. doi: 10.1214/09-AOS781
  • Zhu LP, Li L, Li R, et al. Model-free feature screening for ultrahigh-dimensional data. J Am Stat Assoc. 2011;106(496):1464–1475. doi: 10.1198/jasa.2011.tm10563
  • Li G, Peng H, Zhang J, et al. Robust rank correlation based screening. Ann Stat. 2012;40:1846–1877. doi: 10.1214/12-AOS1024
  • Li R, Zhong W, Zhu L. Feature screening via distance correlation learning. J Am Stat Assoc. 2012;107(499):1129–1139. doi: 10.1080/01621459.2012.695654
  • Lin L, Sun J, Zhu L. Nonparametric feature screening. Comput Stat Data Anal. 2013;67:162–174. doi: 10.1016/j.csda.2013.05.016
  • Chang J, Tang CY, Wu Y. Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. Ann Stat. 2016;44(2):515–539. doi: 10.1214/15-AOS1374
  • Fan J, Ma Y, Dai W. Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models. J Am Stat Assoc. 2014;109(507):1270–1284. doi: 10.1080/01621459.2013.879828
  • Liu J, Li R, Wu R. Feature selection for varying coefficient models with ultrahigh-dimensional covariates. J Am Stat Assoc. 2014;109(505):266–274. doi: 10.1080/01621459.2013.850086
  • Cui H, Li R, Zhong W. Model-free feature screening for ultrahigh dimensional discriminant analysis. J Am Stat Assoc. 2015;110(510):630–641. doi: 10.1080/01621459.2014.920256
  • He X, Wang L, Hong HG. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data. Ann Stat. 2013;41(1):342–369. doi: 10.1214/13-AOS1087
  • 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
  • Li X, Cheng G, Wang L, et al. Ultrahigh dimensional feature screening via projection. Comput Stat Data Anal. 2017;114:88–104. doi: 10.1016/j.csda.2017.04.006
  • Härdle WK, Müller M, Sperlich S, et al. Nonparametric and semiparametric models. Berlin: Springer Series in Statistics; 2004.
  • Ravikumar P, Lafferty J, Liu H, et al. Sparse additive models. J R Stat Soc Ser B (Stat Methodol). 2009;71(5):1009–1030. doi: 10.1111/j.1467-9868.2009.00718.x
  • Stone CJ. Additive regression and other nonparametric models. Ann Stat. 1985;13:689–705. doi: 10.1214/aos/1176349548
  • Zhou S, Shen X, Wolfe DA. Local asymptotics for regression splines and confidence regions. Ann Stat. 1998;26(5):1760–1782. doi: 10.1214/aos/1024691356
  • Lee TI, Rinaldi NJ, Robert F, et al. Transcriptional regulatory networks in saccharomyces cerevisiae. Science. 2002;298(5594):799–804. doi: 10.1126/science.1075090
  • Spellman PT, Sherlock G, Zhang MQ, et al. Comprehensive identification of cell cycle cregulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998;9(12):3273–3297. doi: 10.1091/mbc.9.12.3273
  • Wang L, Chen G, Li H. Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics. 2007;23(12):1486–1494. doi: 10.1093/bioinformatics/btm125

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