Semiparametric regression analysis of multivariate doubly censored data. (October 2020)

• Record Type: Journal Article
• Authors: Li, Shuwei; Hu, Tao; Tong, Tiejun; Sun, Jianguo
• Is Part Of: Statistical modelling. Volume 20:Number 5(2020)
• Page Start: 502
• Page End: 526
• Publication Date: 2020-10
• Subjects: Multivariate doubly censored data -- Maximum likelihood estimation -- frailty model -- semiparametric efficiency -- expectation–maximization algorithm; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X19859949
• Languages: English
• ISSNs: 1471-082X

Abstract

This article discusses regression analysis of multivariate doubly censored data with a wide class of flexible semiparametric transformation frailty models. The proposed models include many commonly used regression models as special cases such as the proportional hazards and proportional odds frailty models. For inference, we propose a nonparametric maximum likelihood estimation method and develop a new expectation–maximization algorithm for its implementation. The proposed estimators of the finite-dimensional parameters are shown to be consistent, asymptotically normal and semiparametrically efficient. We also conduct a simulation study to assess the finite sample performance of the developed estimation method, and the proposed methodology is applied to a set of real data arising from an AIDS study.