TY - JOUR
T1 - Feature screening in ultrahigh-dimensional varying-coefficient Cox model
AU - Yang, Guangren
AU - Zhang, Ling
AU - Li, Runze
AU - Huang, Yuan
N1 - Funding Information:
Yang’s research was supported by the National Nature Science Foundation of China (NNSFC) grants 11471086 and 11871173 , the National Social Science Foundation of China (NSSFC) grant 16BTJ032 , the National Statistical Scientific Center grant 2015LD02 , China; and the Fundamental Research Funds for the Central Universities of Jinan University Qimingxing Plan 15JNQM019 , China. Zhang and Li’s research was supported by National Institute on Drug Abuse (NIDA) grant P50 DA039838 , USA; National Science Foundation (NSF) grant DMS 1820702 , USA; and NNSFC grants 11690014 and 11690015 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDA, the NNSFC, the NSF, or the NSSFC.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/5
Y1 - 2019/5
N2 - The varying-coefficient Cox model is flexible and useful for modeling the dynamic changes of regression coefficients in survival analysis. In this paper, we study feature screening for varying-coefficient Cox models in ultrahigh-dimensional covariates. The proposed screening procedure is based on the joint partial likelihood of all predictors, thus different from marginal screening procedures available in the literature. In order to carry out the new procedure, we propose an effective algorithm and establish its ascent property. We further prove that the proposed procedure possesses the sure screening property. That is, with probability tending to 1, the selected variable set includes the actual active predictors. We conducted simulations to evaluate the finite-sample performance of the proposed procedure and compared it with marginal screening procedures. A genomic data set is used for illustration purposes.
AB - The varying-coefficient Cox model is flexible and useful for modeling the dynamic changes of regression coefficients in survival analysis. In this paper, we study feature screening for varying-coefficient Cox models in ultrahigh-dimensional covariates. The proposed screening procedure is based on the joint partial likelihood of all predictors, thus different from marginal screening procedures available in the literature. In order to carry out the new procedure, we propose an effective algorithm and establish its ascent property. We further prove that the proposed procedure possesses the sure screening property. That is, with probability tending to 1, the selected variable set includes the actual active predictors. We conducted simulations to evaluate the finite-sample performance of the proposed procedure and compared it with marginal screening procedures. A genomic data set is used for illustration purposes.
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U2 - 10.1016/j.jmva.2018.12.009
DO - 10.1016/j.jmva.2018.12.009
M3 - Article
C2 - 31866699
AN - SCOPUS:85059871173
SN - 0047-259X
VL - 171
SP - 284
EP - 297
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
ER -