Censored Rank Independence Screening for High-dimensional Survival Data.

TitleCensored Rank Independence Screening for High-dimensional Survival Data.
Publication TypeJournal Article
Year of Publication2014
AuthorsSong, Rui, Wenbin Lu, Shuangge Ma, and Jessie X Jeng
Date Published2014

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 and Lv, 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 data sets of moderate size and high-dimensional predictors, even when these are contaminated.

Alternate JournalBiometrika
Original PublicationCensored rank independence screening for high-dimensional survival data.
PubMed ID25663709
PubMed Central IDPMC4318124
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA140632 / CA / NCI NIH HHS / United States