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：现有关于负相伴（NA）随机变量的研究大多考虑完全数据情形，这在实际中属于相对理想的条件。本文将这一条件放宽至不完全数据设定，并基于Kaplan-Meier估计量，考虑存在右删失情况下的核平滑密度与风险函数估计。我们建立了这两种估计量的强渐近性质，以评估其渐近行为并验证其实践应用的合理性。