CONFIDENCE INTERVALS FOR HIGH-DIMENSIONAL COX MODELS
Accepted version
Peer-reviewed
Repository URI
Repository DOI
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Authors
Yu, Yi
Bradic, Jelena
Samworth, Richard J
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.
Description
Keywords
Debiased Lasso, High-dimension statistical inference, survival analysis
Journal Title
STATISTICA SINICA
Conference Name
Journal ISSN
1017-0405
1996-8507
1996-8507
Volume Title
31
Publisher
Statistica Sinica (Institute of Statistical Science)
Publisher DOI
Rights
All rights reserved
Sponsorship
Engineering and Physical Sciences Research Council (EP/J017213/1)
Leverhulme Trust (PLP-2014-353)
Engineering and Physical Sciences Research Council (EP/N031938/1)
Engineering and Physical Sciences Research Council (EP/P031447/1)
Leverhulme Trust (PLP-2014-353)
Engineering and Physical Sciences Research Council (EP/N031938/1)
Engineering and Physical Sciences Research Council (EP/P031447/1)