Please use this identifier to cite or link to this item:
Title: Sieve estimation of cox models with latent structures
Authors: Cao, Y
Huang, J 
Liu, Y
Zhao, X 
Keywords: Group selection
Model-pursuit consistency
Modified blockwise majorization descent algorithm
Partially linear Cox model
Penalized partial likelihood
Issue Date: 2016
Publisher: Wiley-Blackwell
Source: Biometrics, 2016, v. 72, no. 4, p. 1086-1097 How to cite?
Journal: Biometrics 
Abstract: This article considers sieve estimation in the Cox model with an unknown regression structure based on right-censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B-spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application.
ISSN: 0006-341X
EISSN: 1541-0420
DOI: 10.1111/biom.12529
Appears in Collections:Journal/Magazine Article

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

Page view(s)

Last Week
Last month
Citations as of Sep 16, 2018

Google ScholarTM



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