We consider smooth nonparametric estimation of the incubation time distribution of COVID-19, in connection with the investigation of researchers from the National Institute for Public Health and the Environment (Dutch: RIVM) of 88 travelers from Wuhan: Backer et al (2020). The advantages of the smooth nonparametric approach w.r.t. the parametric approach, using three parametric distributions (Weibull, log-normal and gamma) in Backer et al (2020) is discussed. It is shown that the typical rate of convergence of the smooth estimate of the density is $n^{2/7}$ in a continuous version of the model, where $n$ is the sample size. The (non-smoothed) nonparametric maximum likelihood estimator (MLE) itself is computed by the iterative convex minorant algorithm (Groeneboom and Jongbloed (2014)). All computations are available as {\tt R} scripts in Groeneboom (2020).

翻译：我们认为,在国家公共卫生和环境研究所(Dutch:RIVM)对来自Wuhan:Backer等人(202020年)的88名旅行者的研究人员进行调查时,对COVID-19(COVID-19)的孵化时间分布进行了顺利的、非参数性的估算,在Backer等人(202020年)使用三种参数分布法(Weibull、log-正常和伽马),对光滑的非参数性方法的优点进行了讨论,显示光滑的密度估计的典型趋同率在模型的连续版本中是$@2/7}$,其中,以美元为样本大小。(非移动的)非对准最大概率估计仪(MLE)本身由迭接微分解算法(Groeneboom和Jongbloed(2014年)计算,所有计算方法均以Groeneboom(2020年)的~R}脚本提供。