We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log-normal or gamma distributions in the estimation procedure. We propose nonparametric estimates which stay closer to the data than the classical parametric methods. We also give explicit limit distributions for discrete versions of the models and apply this to compute confidence intervals. The methods complement the analysis of the continuous model. R scripts for computation of the estimates are provided on https://github.com/pietg/incubationtime.

翻译：我们分析了单次和双次区间删失模型中潜伏期分布函数的非参数估计方法。经典方法通常采用威布尔（Weibull）、对数正态（log-normal）或伽马（gamma）等参数族进行估计。本文提出比经典参数方法更贴近数据的非参数估计量，并给出离散模型的显式极限分布，进而将其应用于置信区间的计算。这些方法补充了对连续模型的分析。相关的R语言计算脚本已提供于 https://github.com/pietg/incubationtime。