<正>Hazard function estimation is an important part of survival analysis.Interest often centers on estimating the hazard function associated with a particular cause of death.We propose three nonparametric kernel estimators for the hazard function,all of which are appropriate when death times are subject to random censorship and censoring indicators can be missing at random.Specifically,we present a regression surrogate estimator,an imputation estimator,and an inverse probability weighted estimator.All three estimators are uniformly strongly consistent and asymptotically normal.We derive asymptotic representations of the mean