Weak Convergence of the Wild Bootstrap for the Aalen-Johansen Estimator of the Cumulative Incidence Function of a Competing Risk
Weak Convergence of the Wild Bootstrap for the Aalen-Johansen Estimator of the Cumulative Incidence Function of a Competing Risk
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DOI：
10.1111/j.1467-9469.2012.00817.x
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发表时间：
2013-09-01
影响因子：
1
通讯作者：
Pauly, Markus
中科院分区：
文献类型：
--
作者：
Beyersmann, Jan;Di Termini, Susanna;Pauly, Markus
。我们严格地研究了竞争风险累积事件概率的非参数估计的野Bootstrap的弱收敛问题。数据可能会受到独立的左截断和右审查的影响。纳入左侧截断手术的动机是一项关于妊娠结局的研究。作为一种情况，野生自举包括一种流行的重采样技术，其中极限分布是通过重复生成标准正态变量来近似的，而数据保持不变。文中还给出了仿真结果和数据实例。
. We give a rigorous study of weak convergence of the wild bootstrap for non-parametric estimation of the cumulative event probability of a competing risk. The data may be subject to independent left-truncation and right-censoring. Inclusion of left-truncation is motivated by a study on pregnancy outcomes. The wild bootstrap includes as one case a popular resampling technique, where the limit distribution is approximated by repeatedly generating standard normal variates, while the data are kept fixed. Simulation results and a data example are also presented.