A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data

References

• Abramovich, F., Benjamini, Y., Donoho, D. L., and Johnstone, I. M. (2006), “Adapting to Unknown Sparsity by Controlling the False Discovery Rate,” The Annals of Statistics, 34, 584–653.
   Web of Science ®Google Scholar
• Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A., Kim, S., Wilson, C., Lehar, J., Kryukov, G., Sonkin, D., Reddy, A., Liu, M., Murray, L., Berger, M., Monahan, J., Morais, P., Meltzer, J., Korejwa, A., Jane-Valbuena, J., Mapa, F., Thibault, J., Bric-Furlong, E., Raman, P., Shipway, A., Engels, I. et al., (2012), “The Cancer Cell Line Encyclopedia Enables Predictive Modelingof Anticancer Drug Sensitivity,” Nature, 483, 603–607.
   PubMed Web of Science ®Google Scholar
• Buzdar, A. (2009), “Role of Biologic Therapy and Chemotherapy in Hormone Receptor and HER2-Positive Breast Cancer,” The Annals of Oncology, 20, 993–999.
   PubMed Web of Science ®Google Scholar
• Cui, H., Li, R., and Zhong, W. (2015), “Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis,” Journal of the American Statistical Association, 110, 630–641.
   PubMed Web of Science ®Google Scholar
• Fan, J., Feng, Y., and Song, R. (2011), “Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models,” Journal of the American Statistical Association, 106, 544–557.
   PubMed Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360.
   Web of Science ®Google Scholar
• Fan, J., and Lv, J. (2008), “Sure Independence Screening for Ultrahigh Dimensional Feature Space,” Journal of the Royal Statistical Society, Series B, 70, 849–911.
   Google Scholar
• Fan, J., Samworth, R., and Wu, Y. (2009), “Ultrahigh Dimensional Feature Selection: Beyond The Linear Model,” Journal of Machine Learning Research, 10, 2013–2038.
   PubMed Web of Science ®Google Scholar
• Fan, J., and Song, R. (2010), “Sure Independence Screening in Generalized Linear Models with NP-Dimensionality,” Annals of Statistics, 38, 3567–3604.
   Web of Science ®Google Scholar
• Grünwald, V., and Hidalgo, M. (2003), “Developing Inhibitors of the Epidermal Growth Factor Receptor for Cancer Treatment,” Journal of the National Cancer Institute, 95, 851–867.
   PubMed Web of Science ®Google Scholar
• Hadley, K. E., and Hendricks, D. T. (2014), “Use of NQO1 Status as a Selective Biomarker for Oesophageal Squamous Cell Carcinomas with Greater Sensitivity to 17-AAG,” BMC Cancer, 14, 1–8.
   PubMed Web of Science ®Google Scholar
• Hall, P., and Miller, H. (2009), “Using Generalized Correlation to Effect Variable Selection in Very High Dimensional Problems,” Journal of Computational and Graphical Statistics, 18, 533–550.
   Web of Science ®Google Scholar
• He, X., Wang, L., and Hong, H. G. (2013), “Correction: Quantile-Adaptive Model-Free Variable Screening for High-Dimensional Heterogeneous Data,” The Annals of Statistics, 41, 2699.
   Web of Science ®Google Scholar
• Henze, N., and Zirkler, B. (1990), “A Class of Invariant Consistent Tests for Multivariate Normality,” Communications in Statistics---Theory and Methods, 10, 3595–3617.
   Web of Science ®Google Scholar
• Huang, J., Horowitz, J. L., and Ma, S. (2008), “Asymptotic Properties of Bridge Estimators in Sparse High-Dimensional Regression Models,” Annals of Statistics, 36, 587–613.
   Web of Science ®Google Scholar
• Jiang, B., and Liu, J. (2014), “Sliced Inverse Regression with Variable Selection andInteraction Detection,” Annals of Statistics, 42, 1751–1786.
   Web of Science ®Google Scholar
• Li, R., Zhong, W., and Zhu, L. (2012), “Feature Screening via Distance Correlation Learning,” Journal of the American Statistical Association, 107, 1129–1139.
   PubMed Web of Science ®Google Scholar
• Liang, F. (2008), “Clustering Gene Expression Profiles Using Mixture Model Ensemble Averaging Approach,” Journal of Biostatistics, 2, 57–80.
   Google Scholar
• Liu, H., Lafferty, J., and Wasserman, L. (2009), “The nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs,” Journal of Machine Learning Research, 10, 2295–2328.
   Web of Science ®Google Scholar
• Mardia, K. (1970), “Measures of Multivariate Skewness and Kurtosis with Applications,” Biometrika, 57, 519–530.
   Web of Science ®Google Scholar
• Serfling, R. (1980), Approximation Theorems of Mathematical Statistics–(Wiley Series in Probability and Statistics---Applied Probability and Statistics Section Series), New York: Wiley.
   Google Scholar
• Székely, G., and Rizzo, M. (2005), “A New Test for Multivariate Normality,” Journal of Multivariate Analysis, 93, 58–80.
   Web of Science ®Google Scholar
• Székely, G., Rizzo, M., and Bakirov, N. (2007), “Measuring and Testing Dependence by Correlation of Distances,” The Annals of Statistics, 35, 2769–2794.
   Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the LASSO,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Wang, K., Shrestha, R., Wyatt, A. W., Reddy, A., Lehr, J., Wang, Y., Lapuk, A., and Collins, C. C. (2014), “A Meta-Analysis Approach for Characterizing Pan-Cancer Mechanisms of Drug Sensitivity in Cell Lines,” PLoS One, 9, 1–16.
   Web of Science ®Google Scholar
• Zhang, C.-H. (2010), “Nearly Unbiased Variable Selection Under Minimax Concave Penalty,” The Annals of Statistics, 38, 894–942.
   Web of Science ®Google Scholar
• Zhu, L.-P., Li, L., Li, R., and Zhu, L.-X. (2011), “Model-Free Feature Screening for Ultrahigh-Dimensional Data,” Journal of the American Statistical Association, 106, 1464–1475.
   PubMed Web of Science ®Google Scholar
• Zoppoli, G., Regairaz, M., Leo, E., Reinhold, W., Varma, S., Ballestrero, A., Doroshow, J., and Pommier, Y. (2012), “Putative DNA/RNA Helicase Schlafenl1 (SLFN11) Sensitizes Cancer Cells to DNA-Damaging Agents,” Proceedings of the National Academy of Sciences USA, 109, 15030–15035.
   PubMed Web of Science ®Google Scholar