  Kernel-based nonparametric hazard rate estimation is considered with a
special class of infinite-order kernels that achieves favorable bias and mean
square error properties. A fully automatic and adaptive implementation of a
density and hazard rate estimator is proposed for randomly right censored data.
Careful selection of the bandwidth in the proposed estimators yields estimates
that are more efficient in terms of overall mean squared error performance, and
in some cases achieves a nearly parametric convergence rate. Additionally,
rapidly converging bandwidth estimates are presented for use in second-order
kernels to supplement such kernel-based methods in hazard rate estimation.
Simulations illustrate the improved accuracy of the proposed estimator against
other nonparametric estimators of the density and hazard function. A real data
application is also presented on survival data from 13,166 breast carcinoma
patients.
