IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v99y2012i3p533-550.html
   My bibliography  Save this article

Inferring stochastic dynamics from functional data

Author

Listed:
  • Nicolas Verzelen
  • Wenwen Tao
  • Hans-Georg Müller

Abstract

In most current data modelling for time-dynamic systems, one works with a prespecified differential equation and attempts to estimate its parameters. In contrast, we demonstrate that in the case of functional data, the equation itself can be inferred. Assuming only that the dynamics are described by a first-order nonlinear differential equation with a random component, we obtain data-adaptive dynamic equations from the observed data via a simple smoothing-based procedure. We prove consistency and introduce diagnostics to ascertain the fraction of variance that is explained by the deterministic part of the equation. This approach is shown to yield useful insights into the time-dynamic nature of human growth. Copyright 2012, Oxford University Press.

Suggested Citation

  • Nicolas Verzelen & Wenwen Tao & Hans-Georg Müller, 2012. "Inferring stochastic dynamics from functional data," Biometrika, Biometrika Trust, vol. 99(3), pages 533-550.
  • Handle: RePEc:oup:biomet:v:99:y:2012:i:3:p:533-550
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/ass015
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hao, Siteng & Lin, Shu-Chin & Wang, Jane-Ling & Zhong, Qixian, 2024. "Dynamic modeling for multivariate functional and longitudinal data," Journal of Econometrics, Elsevier, vol. 239(2).
    2. Matthew Reimherr & Dan Nicolae, 2016. "Estimating Variance Components in Functional Linear Models With Applications to Genetic Heritability," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 407-422, March.

    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:oup:biomet:v:99:y:2012:i:3:p:533-550. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.