Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease.

ABSTRACT: Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right-censored failure time data. In this article, we discuss an interval-censored failure time situation where there exist two sets of covariates with one being low-dimensional and having possible nonlinear effects and the other being high-dimensional. For the problem, we present a penalized estimation procedure for simultaneous variable selection and estimation, and in the method, Bernstein polynomials are used to approximate the involved nonlinear functions. Furthermore, for implementation, a coordinate-wise optimization algorithm, which can accommodate most commonly used penalty functions, is developed. A numerical study is performed for the evaluation o

SUBMITTER: Wu Q 

PROVIDER: S-EPMC7936877 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature