Statistical Modelling 20 (5) (2020), 502–526
Semiparametric regression analysis of multivariate doubly censored data
Shuwei Li,
School of Economics and Statistics,
Guangzhou University,
Guangzhou,
China.
Tao Hu,
School of Mathematical Sciences,
Capital Normal University,
Beijing,
China.
e-mail: hutaomath@foxmail.com
Tiejun Tong,
Department of Mathematics,
Hong Kong Baptist University,
Hong Kong.
Jianguo Sun,
Department of Statistics,
University of Missouri,
Columbia, Missouri,
USA.
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.
Keywords:
Multivariate doubly censored data, Maximum likelihood estimation, frailty model, semiparametric efficiency, expectation–maximization algorithm.
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