Censored rank independence screening for high-dimensional survival data

• 成果类型: Article
• 署名作者: Song, Rui; Lu, Wenbin; Ma, Shuangge; Jeng, X. Jessie
• 署名单位: North Carolina State University; Yale University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu047
• 发表日期: 2014
• 页码: 799814
• 关键词: GENERALIZED LINEAR-MODELS; variable selection; regression; Lasso; inequalities

摘要

In modern statistical applications, the dimension of covariates can be much larger than the sample size. In the context of linear models, correlation screening (Fan & Lv, J. R. Statist. Soc. B, 70, 849-911, 2008) has been shown to reduce the dimension of such data effectively while achieving the sure screening property, i.e., all of the active variables can be retained with high probability. However, screening based on the Pearson correlation does not perform well when applied to contaminated covariates and/or censored outcomes. In this paper, we study censored rank independence screening of high-dimensional survival data. The proposed method is robust to predictors that contain outliers, works for a general class of survival models, and enjoys the sure screening property. Simulations and an analysis of real data demonstrate that the proposed method performs competitively on survival datasets of moderate size and high-dimensional predictors, even when these are contaminated.