[博SCI]Nonparametric estimation of current status data with dependent censoring

Release time:2021-04-16  Hits:

• Affiliation of Author(s):数学与统计学院
• Teaching and Research Group:统计教研室
• Journal:Lifetime Data Analysis
• Funded by:国家自然科学基金项目
• Key Words:Competing risks , Copula models, Current status data, Nonparametric
• Abstract:Nonparametric estimation of a survival function is often the first task in the analysis of failure time data and this paper discusses this problem when one observes only current status data (McKeown and Jewell, 2010; Sun, 2006). In this situation, each subject is observed only once and the failure time of interest is either left- or right-censored. If the failure time and the observation time can be assumed to be independent, the maximum likelihood estimate of the survival function can be easily derived. However, the independent assumption may not hold in practice and with dependent observation time, it becomes a competing risk problem although not a standard one with current status data.For the problem, we employ a copula model, a natural and commonly used approach for competing risks, and show that if the copula function is known, the survival function of interest can be consistently and uniquely estimated. Two simple estimates are proposed and an simulation study is performed to assess their performance.
• Co-author:Sun,Jianguo,孙六全,周杰,王德辉
• First Author:wangchunjie
• Indexed by:Journal paper
• Volume:18
• Issue:4
• Page Number:1
• ISSN No.:1380-7870
• Translation or Not:no
• Date of Publication:2012-10-01