Statistics and Its Interface
Volume 14 (2021)
A non-marginal variable screening method for the varying coefficient Cox model
Pages: 197 – 209
The varying coefficient model has become a very popular statistical tool for describing the dynamic effects of covariates on the response. In this article, we develop a new variable screening method for the varying coefficient Cox model based on the kernel smoothing and group learning methods. The sure screening property is established for ultra-high-dimensional settings. In addition, an iterative groupwise hard-thresholding algorithm is developed to implement our method. Simulation studies are conducted to evaluate the finite sample performances of the proposed method. An application to an ovarian cancer dataset is provided.
Cox model, kernel smoothing, non-marginal screening, ultra-high-dimensionality, varying coefficient
2010 Mathematics Subject Classification
This research was partly supported by the National Natural Science Foundation of China (Grant Nos. 11771431 and 11690015), Key Laboratory of RCSDS, CAS (No. 2008DP173182), and by the Hubei Natural Science Foundation of China (Grant No. 2018CFB256).
Received 30 August 2019
Accepted 15 July 2020
Published 22 December 2020