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).

中文翻译：

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估计 COVID ‐19 的潜伏期分布

我们考虑了对 COVID-19 潜伏时间分布的平滑非参数估计，结合国家公共卫生与环境研究所（荷兰语：RIVM）的研究人员对来自武汉的 88 名旅客的调查：Backer 等人（2020 年）。讨论了使用 Backer 等人 (2020) 中的三个参数分布（Weibull、对数正态分布和 gamma）的平滑非参数方法与参数方法相比的优势。结果表明，在模型的连续版本中，密度平滑估计的典型收敛速度为 $n^{2/7}$，其中 $n$ 是样本大小。（非平滑）非参数最大似然估计器 (MLE) 本身由迭代凸次要算法（Groeneboom 和 Jongbloed (2014)）计算。