|Title||Censored Rank Independence Screening for High-dimensional Survival Data.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Song, Rui, Wenbin Lu, Shuangge Ma, and Jessie X Jeng|
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.
|Original Publication||Censored rank independence screening for high-dimensional survival data.|
|PubMed Central ID||PMC4318124|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA140632 / CA / NCI NIH HHS / United States
Censored Rank Independence Screening for High-dimensional Survival Data.