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Regression analysis of informative current status data with the semiparametric linear transformation model

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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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Keywords: Bernstein polynomial; Linear transformation model; current status data; efficient estimation; informative censoring

Document Type: Research Article

Affiliations: 1: Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, People's Republic of China 2: School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China 3: Department of Statistics, University of Missouri, Columbia, MO, USA

Publication date: January 25, 2019

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