Estimation of the incubation time distribution for COVID-19

Piet Groeneboom*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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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 with respect to 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 n2/7 in a continuous version of the model, where n is the sample size. The (nonsmoothed) nonparametric maximum likelihood estimator itself is computed by the iterative convex minorant algorithm (Groeneboom and Jongbloed (2014)). All computations are available as R scripts in Groeneboom (2020a).

Original languageEnglish
Pages (from-to)161-179
Number of pages19
JournalStatistica Neerlandica
Issue number2
Publication statusPublished - 2020


  • incubation time
  • iterative convex minorant algorithm
  • nonparametric MLE
  • smooth nonparametric density estimation
  • Weibull distribution


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