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摘要翻译：
基于核的非参数危险率估计具有一类特殊的无穷阶核，它具有良好的偏差和均方误差性质。针对随机右删失数据，提出了一种全自动、自适应的密度和危险率估计器。在所提出的估计器中谨慎地选择带宽产生了在总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几乎参数化的收敛速度。此外，还提出了用于二阶核的快速收敛带宽估计，以补充基于核的危险率估计方法。仿真结果表明，与密度和危险函数的其他非参数估计相比，该估计器的精度有所提高。一个真实的数据应用也提出了13，166乳腺癌患者的生存数据。
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英文标题：
《Reduced bias nonparametric lifetime density and hazard estimation》
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作者：
Arthur Berg and Dimitris N Politis and Kagba Suaray and Hui Zeng
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最新提交年份：
2018
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/704.3281
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