Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78254
Title: On the sign consistency of the Lasso for the high-dimensional Cox model
Authors: Lv, SG
You, MY
Lin, HZ
Lian, H
Huang, J 
Keywords: Cox proportional
Empirical process
Hazard model
Lasso
Mutual coherence
Oracle property
Sparse recovery
Issue Date: 2018
Publisher: Academic Press
Source: Journal of multivariate analysis, Sept. 2018, v. 167, p. 79-96 How to cite?
Journal: Journal of multivariate analysis 
Abstract: In this paper we study the l(1)-penalized partial likelihood estimator for the sparse high dimensional Cox proportional hazards model. In particular, we investigate how the l(1)-penalized partial likelihood estimation recovers the sparsity pattern and the conditions under which the sign support consistency is guaranteed. We establish sign recovery consistency and l(infinity)-error bounds for the Lasso partial likelihood estimator under suitable and interpretable conditions, including mutual incoherence conditions. More importantly, we show that the conditions of the incoherence and bounds on the minimal non-zero coefficients are necessary, which provides significant and instructional implications for understanding the Lasso for the Cox model. Numerical studies are presented to illustrate the theoretical results.
URI: http://hdl.handle.net/10397/78254
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2018.04.005
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

8
Citations as of Dec 3, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.