Confidence intervals for high-dimensional Cox models
Methodology
2018-03-06 v1 Statistics Theory
Statistics Theory
Abstract
The purpose of this paper is to construct confidence intervals for the regression coefficients in high-dimensional Cox proportional hazards regression models where the number of covariates may be larger than the sample size. Our debiased estimator construction is similar to those in Zhang and Zhang (2014) and van de Geer et al. (2014), but the time-dependent covariates and censored risk sets introduce considerable additional challenges. Our theoretical results, which provide conditions under which our confidence intervals are asymptotically valid, are supported by extensive numerical experiments.
Cite
@article{arxiv.1803.01150,
title = {Confidence intervals for high-dimensional Cox models},
author = {Yi Yu and Jelena Bradic and Richard J. Samworth},
journal= {arXiv preprint arXiv:1803.01150},
year = {2018}
}
Comments
36 pages, 1 figure