Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$61.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

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

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more