Journal article
Sieve estimation of Cox models with latent structures
Biometrics, Vol.72(4), pp.1086-1097
12/2016
DOI: 10.1111/biom.12529
PMID: 27385420
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.
Details
- Title: Subtitle
- Sieve estimation of Cox models with latent structures
- Creators
- Yongxiu Cao - Wuhan UniversityJian Huang - University of IowaYanyan Liu - Wuhan UniversityXingqiu Zhao - Hong Kong Polytechnic University
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.72(4), pp.1086-1097
- DOI
- 10.1111/biom.12529
- PMID
- 27385420
- ISSN
- 0006-341X
- eISSN
- 1541-0420
- Grant note
- name: Program for Changjiang Scholars and Innovative Research Team, award: IRT13077; name: Research Grant Council of Hong Kong, award: 503513; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 11371299
- Language
- English
- Date published
- 12/2016
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257611802771
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