Many methods have been developed in the literature for regression analysis of current status data with noninformative censoring and also some approaches have been proposed for semiparametric regression analysis of current status data with informative censoring. However, the existing
approaches for the latter situation are mainly on specific models such as the proportional hazards model and the additive hazard model. Corresponding to this, in this paper, we consider a general class of semiparametric linear transformation models and develop a sieve maximum likelihood estimation
approach for the inference. In the method, the copula model is employed to describe the informative censoring or relationship between the failure time of interest and the censoring time, and Bernstein polynomials are used to approximate the nonparametric functions involved. The asymptotic
consistency and normality of the proposed estimators are established, and an extensive simulation study is conducted and indicates that the proposed approach works well for practical situations. In addition, an illustrative example is provided.
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Linear transformation model;
current status data;
Document Type: Research Article
Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China
School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China
Department of Statistics, University of Missouri, Columbia, MO, USA
Publication date: January 25, 2019