title: Estimating the survival function based on the semi-Markov model for dependent censoring.
abstract: In this paper, we study a nonparametric maximum likelihood estimator (NPMLE) of the survival function based on a semi-Markov model under dependent censoring. We show that the NPMLE is asymptotically normal and achieves asymptotic nonparametric efficiency. We also provide a uniformly consistent estimator of the corresponding asymptotic covariance function based on an information operator. The finite-sample performance of the proposed NPMLE is examined with simulation studies, which show that the NPMLE has smaller mean squared error than the existing estimators and its corresponding pointwise confidence intervals have reasonable coverages. A real example is also presented.
authors.affiliation: Department of Statistics, School of Management, Fudan University, 670 Guoshun Road, Shanghai, China.
authors.forename: Ziqiang
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: Department of Statistics, School of Management, Fudan University, 670 Guoshun Road, Shanghai, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Zheng
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, USA. zj7@columbia.edu.
authors.forename: Zhezhen
authors.initials: Z
authors.lastname: Jin
mesh_terms: D001699:Biometry; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D053208:Kaplan-Meier Estimate; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000401:mortality; D008390:Markov Chains; D015233:Models, Statistical; D018709:Statistics, Nonparametric; D016019:Survival Analysis
keywords: Dependent censoring; NPMLE; Semi-Markov model; Survival function