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- W3088049369 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 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)." @default.
- W3088049369 created "2020-10-01" @default.
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- W3088049369 date "2020-09-26" @default.
- W3088049369 modified "2023-09-27" @default.
- W3088049369 title "Estimation of the incubation time distribution for COVID-19" @default.
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