Strong Asymptotic Properties of Kernel Smooth Density and Hazard Function Estimation for Right Censoring NA Random Variable

Most studies for NA random variable is under complete sampling setting, which is actually an relatively ideal condition in application. The paper relaxes this condition to the censoring incomplete sampling data and considers the topic for kernel estimation of the density function together with the hazard function based on the Kaplan-Meier estimator. The strong asymptotic properties for the two estimators are firstly established.