Abstract
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 language | English |
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Pages (from-to) | 161-179 |
Number of pages | 19 |
Journal | Statistica Neerlandica |
Volume | 75 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- incubation time
- iterative convex minorant algorithm
- nonparametric MLE
- smooth nonparametric density estimation
- Weibull distribution