The Estimation of the Hazard Function from Randomly Censored Data by the Kernel Method

Abstract

By convolution smoothing of the empirical hazards, a kernel estimate of the hazard function from censored data is obtained. Small and large sample expressions for the mean and the variance of the estimator are given. Conditions for asymptotic normality are investigated using the Hajek projection method.