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Estimation of the incubation time distribution for COVID‐19

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  • Piet Groeneboom

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).

Suggested Citation

  • Piet Groeneboom, 2021. "Estimation of the incubation time distribution for COVID‐19," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 161-179, May.
    • Handle: RePEc:bla:stanee:v:75:y:2021:i:2:p:161-179
    DOI: 10.1111/stan.12231
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    References listed on IDEAS

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    1. Groeneboom,Piet & Jongbloed,Geurt, 2014. "Nonparametric Estimation under Shape Constraints," Cambridge Books, Cambridge University Press, number 9780521864015.
    2. Hall, Peter, 1990. "Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 177-203, February.
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