IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v35y2023i1p172-197.html
   My bibliography  Save this article

Functional linear model with partially observed covariate and missing values in the response

Author

Listed:
  • Christophe Crambes
  • Chayma Daayeb
  • Ali Gannoun
  • Yousri Henchiri

Abstract

Dealing with missing values is an important issue in data observation or data recording process. In this paper, we consider a functional linear regression model with partially observed covariate and missing values in the response. We use a reconstruction operator that aims at recovering the missing parts of the explanatory curves, then we are interested in regression imputation method of missing data on the response variable, using functional principal component regression to estimate the functional coefficient of the model. We study the asymptotic behaviour of the prediction error when missing data are replaced by the imputed values in the original dataset. The practical behaviour of the method is also studied on simulated data and a real dataset.

Suggested Citation

  • Christophe Crambes & Chayma Daayeb & Ali Gannoun & Yousri Henchiri, 2023. "Functional linear model with partially observed covariate and missing values in the response," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 35(1), pages 172-197, January.
    • Handle: RePEc:taf:gnstxx:v:35:y:2023:i:1:p:172-197
    DOI: 10.1080/10485252.2022.2142222
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2022.2142222
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2022.2142222?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
    ---><---

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

    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:taf:gnstxx:v:35:y:2023:i:1:p:172-197. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GNST20 .

    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.