  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.
