IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v34y2025i1d10.1007_s11749-024-00957-3.html
   My bibliography  Save this article

Semi-functional partial linear regression with measurement error: an approach based on kNN estimation

Author

Listed:
  • Silvia Novo

    (Universidad Carlos III de Madrid
    Instituto Flores de Lemus
    UC3M-Santander Big Data Institute)

  • Germán Aneiros

    (Universidade da Coruña
    Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC))

  • Philippe Vieu

    (Université Paul Sabatier)

Abstract

This paper focuses on a semi-parametric regression model in which the response variable is explained by the sum of two components. One of them is parametric (linear), the corresponding explanatory variable is measured with additive error and its dimension is finite (p). The other component models, in a nonparametric way, the effect of a functional variable (infinite dimension) on the response. kNN-based estimators are proposed for each component, and some asymptotic results are obtained. A simulation study illustrates the behaviour of such estimators for finite sample sizes, while an application to real data shows the usefulness of our proposal.

Suggested Citation

  • Silvia Novo & Germán Aneiros & Philippe Vieu, 2025. "Semi-functional partial linear regression with measurement error: an approach based on kNN estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(1), pages 235-261, March.
    • Handle: RePEc:spr:testjl:v:34:y:2025:i:1:d:10.1007_s11749-024-00957-3
    DOI: 10.1007/s11749-024-00957-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11749-024-00957-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11749-024-00957-3?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.

    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:spr:testjl:v:34:y:2025:i:1:d:10.1007_s11749-024-00957-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.