Dinse G E
Biometrics. 1982 Jun;38(2):417-31.
Many statistical models focus on a random variable that represents time until failure and an indicator variable that denotes type of failure. When censoring mechanisms are introduced, an incomplete observation on the failure time often precludes observation of the indicator. In addition to conventional outcomes, for which observations on the time until failure and the type of failure are both complete or both incomplete, this paper considers partially-complete outcomes, for which only one of the random variables if fully observed. An iterative algorithm yields distribution-free estimates of the joint law governing this random pair; these estimates converge to the maximum likelihood solution. Recent developments permit approximations to the information and covariance matrices. Several special cases lead to closed-form estimates of the underlying distribution. Data from two recent clinical trials are used to illustrate the proposed techniques.
许多统计模型关注一个表示失效时间的随机变量和一个表示失效类型的指示变量。当引入删失机制时，对失效时间的不完整观测常常使指示变量无法被观测到。除了传统的结果（即失效时间和失效类型的观测都是完整的或都是不完整的）之外，本文还考虑了部分完整的结果，即只有一个随机变量是完全观测到的。一种迭代算法可得出控制这一随机变量对的联合分布的无分布估计；这些估计收敛于最大似然解。最近的进展允许对信息矩阵和协方差矩阵进行近似。几个特殊情况可得出基础分布的闭式估计。来自两项近期临床试验的数据被用于说明所提出的技术。
