Online estimation of hazard rate under random censoring

We introduce a recursive kernel estimator of the hazard function in the framework of independent rightly censored data. We compute its bias and variance, and compare its mean squared error to those of non-recursive kernel estimators. We also establish its weak convergence rate and point out that, for estimation by confidence intervals, our recursive estimator performs better than the non-recursive ones. This is confirmed by the simulations study we give.