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 square error performance, and in some cases, a nearly parametric convergence rate is achieved. 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.

中文翻译：

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减少偏差非参数寿命密度和危险估计。

基于内核的非参数风险率估计被考虑使用一类特殊的无限阶内核，该内核可实现有利的偏差和均方误差特性。针对随机右删失数据提出了一种全自动和自适应的密度和危险率估计器实现。在建议的估计器中仔细选择带宽会产生在整体均方误差性能方面更有效的估计，并且在某些情况下，实现了接近参数化的收敛速度。此外，还提供了快速收敛的带宽估计，用于二阶核，以补充这种基于核的方法在危险率估计中的应用。模拟表明，相对于密度和危险函数的其他非参数估计量，所提出的估计量提高了准确性。