Efficient estimation from right-censored data when failure indicators are missing at random

• 成果类型: Article
• 署名作者: van der Laan, MJ; McKeague, IW
• 署名单位: University of California System; University of California Berkeley; State University System of Florida; Florida State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 164-182
• 关键词: survival function; models; time

摘要

The Kaplan-Meier estimator of a survival function is well known to be asymptotically efficient when cause of failure is always observed. It has been an open problem, however, to find an efficient estimator when failure indicators are missing at random. Lo showed that nonparametric maximum likelihood estimators are inconsistent, and this has led to several proposals of ad hoc estimators, none of which are efficient. We now introduce a sieved nonparametric maximum likelihood estimator, and show that it is efficient. Our approach is related to the Estimation of a bivariate survival function from bivariate right-censored data.