论文标题
COVID-19的孵育时间分布的估计
Estimation of the incubation time distribution for COVID-19
论文作者
论文摘要
我们考虑对Covid-19的孵育时间分布的平稳估计，与国家公共卫生与环境研究所（荷兰：RIVM）的研究人员的调查有关，来自武汉：Backer等人（2020年）。平滑非参数方法的优势W.R.T.讨论了使用三个参数分布（Weibull，Log-Normal和Gammal和Gamma）（2020）中的参数方法。 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率为$ n^{2/7} $，其中$ n $是样本量。 （非平滑）非参数最大似然估计量（MLE）本身是由迭代凸次小型算法计算的（Groeneboom and Jongbloed（2014））。所有计算均以Groeneboom（2020）中的{\ tt r}脚本提供。
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).
