IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v75y2021i2p161-179.html
   My bibliography  Save this article

Estimation of the incubation time distribution for COVID‐19

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/stan.12231
    Download Restriction: no

    File URL: https://libkey.io/10.1111/stan.12231?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Groeneboom,Piet & Jongbloed,Geurt, 2014. "Nonparametric Estimation under Shape Constraints," Cambridge Books, Cambridge University Press, number 9780521864015, October.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danielsson, J. & de Haan, L. & Peng, L. & de Vries, C. G., 2001. "Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation," Journal of Multivariate Analysis, Elsevier, vol. 76(2), pages 226-248, February.
    2. Barunik, Jozef & Vacha, Lukas, 2010. "Monte Carlo-based tail exponent estimator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4863-4874.
    3. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    4. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    5. Mao, Lu, 2022. "Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction," Statistics & Probability Letters, Elsevier, vol. 189(C).
    6. Ruixuan Liu & Zhengfei Yu, 2019. "Simple Semiparametric Estimation of Ordered Response Models: with an Application to the Interdependence Duration Models," Tsukuba Economics Working Papers 2019-004, Faculty of Humanities and Social Sciences, University of Tsukuba.
    7. Yoici Arai & Taisuke Otsu & Mengshan Xu, 2022. "GLS under monotone heteroskedasticity," STICERD - Econometrics Paper Series 625, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    8. Adriano Z. Zambom & Ronaldo Dias, 2013. "A Review of Kernel Density Estimation with Applications to Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 20-42, April.
    9. Dominique Guegan & Bertrand K. Hassani, 2011. "Operational risk: a Basel II++ step before Basel III," Documents de travail du Centre d'Economie de la Sorbonne 11053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    10. Christian Schluter, 2021. "On Zipf’s law and the bias of Zipf regressions," Empirical Economics, Springer, vol. 61(2), pages 529-548, August.
    11. Josep Lluís Carrion-i-Silvestre & Andreu Sansó, 2023. ""Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series"," IREA Working Papers 202309, University of Barcelona, Research Institute of Applied Economics, revised Jul 2023.
    12. Juan M. Vilar Fernández & Alejandro Quintela del Río, 1993. "Técnicas no paramétricas de estimación funcional, con observaciones dependientes," Investigaciones Economicas, Fundación SEPI, vol. 17(1), pages 143-163, January.
    13. Franke, Reiner & Westerhoff, Frank, 2012. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
    14. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    15. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    16. Max Köhler & Anja Schindler & Stefan Sperlich, 2014. "A Review and Comparison of Bandwidth Selection Methods for Kernel Regression," International Statistical Review, International Statistical Institute, vol. 82(2), pages 243-274, August.
    17. Mahfuzul Haque & Oscar Varela, 2010. "US-Thailand Bilateral Safety-first Portfolio Optimisation around the 1997 Asian Financial Crisis," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 9(2), pages 171-197, August.
    18. González-Sánchez, Mariano & Nave Pineda, Juan M., 2023. "Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement," International Review of Financial Analysis, Elsevier, vol. 86(C).
    19. Elina Robeva & Bernd Sturmfels & Ngoc Tran & Caroline Uhler, 2021. "Maximum likelihood estimation for totally positive log‐concave densities," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 817-844, September.
    20. Chan, Ngai-Hang & Lee, Thomas C.M. & Peng, Liang, 2010. "On nonparametric local inference for density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 509-515, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:stanee:v:75:y:2021:i:2:p:161-179. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.