Journal article
SEMIPARAMETRIC INFERENCE FOR THE PROPORTIONAL MEAN RESIDUAL LIFE MODEL WITH RIGHT-CENSORED LENGTH-BIASED DATA
Statistica Sinica, Vol.26(3), pp.1129-1158
07/01/2016
DOI: 10.5705/ss.2013.117
Abstract
We propose a semiparametric inference approach for proportional mean residual life model with right-censored length-biased data, that arise frequently in observational studies, especially in epidemiological cohort studies. A challenge in the analysis of such data is the presence of informative censoring. Another challenge is that the distribution of the observed data is different from that of the underlying model. We develop an inverse probability weighted approach to estimation based on estimating equations. We establish large sample properties and study the semiparametric efficiency and double robustness property of the proposed estimators. We also propose an improved estimator that chooses the most efficient one in the class of augmented inverse probability weighted estimators. We use simulation studies to evaluate the proposed method, and illustrate its application using a data analysis.
Details
- Title: Subtitle
- SEMIPARAMETRIC INFERENCE FOR THE PROPORTIONAL MEAN RESIDUAL LIFE MODEL WITH RIGHT-CENSORED LENGTH-BIASED DATA
- Creators
- Fangfang Bai - Univ Int Business & Econ, Sch Stat, Beijing 100029, Peoples R ChinaJian Huang - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R ChinaYong Zhou - Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.26(3), pp.1129-1158
- Publisher
- STATISTICA SINICA
- DOI
- 10.5705/ss.2013.117
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Number of pages
- 30
- Grant note
- Shanghai Firstclass Discipline A Program for Innovative Research Team in UIBE IRT13077 / IRTSHUFE, PCSIRT 11501104; 71271128 / National Natural Science Foundation of China (NSFC) Key Laboratory of RCSDS, CAS NCMIS 211 / Shanghai University of Finance and Economics 71331006 / State Key Program of National Natural Science Foundation of China
- Language
- English
- Date published
- 07/01/2016
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257617202771
Metrics
3 Record Views