Most studies for negatively associated (NA) random variables consider the complete-data situation, which is actually a relatively ideal condition in practice. The paper relaxes this condition to the incomplete-data setting and considers kernel smoothing density and hazard function estimation in the presence of right censoring based on the Kaplan-Meier estimator. We establish the strong asymptotic properties for these two estimators to assess their asymptotic behavior and justify their practical use.
翻译:大部分与负相关(NA)随机变量的研究都考虑了完整数据状况,这实际上是一个相对理想的条件。该文件将这一条件放松到不完整的数据设置中,并在根据卡普兰-梅耶估测器进行右侧检查的情况下,考虑内核平滑密度和危险功能估计。我们为这两个估测器确定了很强的无药性特性,以评估其无药可治行为,并证明其实际使用的理由。