A surrogate ℓ₀ sparse Cox's regression with applications to sparse high-dimensional massive sample size time-to-event data.

ABSTRACT: Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ₀ -based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of ℓ₂ -penalized regression method for sHDMSS time-to-event data. Specifically, we extend the ℓ₀ -based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ₂ -penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, ora

SUBMITTER: Kawaguchi ES 

PROVIDER: S-EPMC8386178 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature