Feature screening for ultrahigh dimensional categorical data with covariates missing at random

Cheng, 2018, Feature screening in ultrahigh dimensinal categorical data based on conditional information entropy, Stat. Decis., 500, 64 Cui, 2015, Model-free feature screening for ultrahigh dimensional discriminant analysis, J. Amer. Statist. Assoc., 110, 630, 10.1080/01621459.2014.920256 Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc., 96, 1348, 10.1198/016214501753382273 Fan, 2008, Sure independence screening for ultrahigh dimensional feature space (with discussion), J.R. Stat. Soc. Ser. B, 70, 849, 10.1111/j.1467-9868.2008.00674.x Fan, 2014, Nonparametric independence screening in sparse ultrahigh dimensional varying coefficient models, J. Amer. Statist. Assoc., 109, 1270, 10.1080/01621459.2013.879828 Fan, 2010, Sure independence screening in generalized linear models with NP-dimensionality, Ann. Statist., 38, 3567, 10.1214/10-AOS798 Fang, 2016, Model selection with nonignorable nonresponse, Biometrika, 103, 861, 10.1093/biomet/asw039 Garcia, 2010, Variable selection for regression models with missing data, Statist. Sinica, 20, 149 Huang, 2014, Feature screening for ultrahigh dimensional categorical data with applications, J. Bus. Econ. Stat., 32, 237, 10.1080/07350015.2013.863158 Ibrahim, 2008, Model selection criteria for missing-data problems using the EM algorithm, J. Amer. Statist. Assoc., 103, 1648, 10.1198/016214508000001057 Kim, 2013 Lai, 2017, Model free feature screening for ultrahigh dimensional data with responses missing at random, Comput. Statist. Data Anal., 105, 201, 10.1016/j.csda.2016.08.008 Li, 2012, Feature screening via distance correlation learning, J. Amer. Statist. Assoc., 107, 1129, 10.1080/01621459.2012.695654 Little, 2002 Mai, 2013, The Kolmogorov filter for variable screening in high-dimensional binary classification, Biometrika, 100, 229, 10.1093/biomet/ass062 Mai, 2015, The fused Kolmogorov filter: A nonparametric model-free screening method, Ann. Statist., 43, 1471, 10.1214/14-AOS1303 Ni, 2016, Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification, J. Nonparametr. Stat., 28, 515, 10.1080/10485252.2016.1167206 Ni, 2017, Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data, Metrika, 80, 805, 10.1007/s00184-017-0629-9 Pan, 2016, Ultrahigh-dimensional multiclass linear discriminant analysis by pairwise sure independence screening, J. Amer. Statist. Assoc., 111, 169, 10.1080/01621459.2014.998760 Quinlan, 1992 Tibshirani, 1996, Regression shrinkage and selection via the LASSO, J. R. Stat. Soc. Ser. B Stat. Methodol., 58, 267 Van der Vaart, 1996 Wang, 2009, Forward regression for ultrahigh dimensional variable screening, J. Amer. Statist. Assoc., 104, 1512, 10.1198/jasa.2008.tm08516 Wang, 2018, How to make model free feature screening approaches for full data applicable to the case of missing response?, Scand. J. Stat., 45, 324, 10.1111/sjos.12290 Zhao, 2018, Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data, Statist. Sinica, 28, 2125 Zhu, 2011, Model-free feature screening for ultrahigh-dimensional data, J. Amer. Statist. Assoc., 106, 1464, 10.1198/jasa.2011.tm10563