IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v17y2025i1d10.1007_s12561-023-09373-x.html
   My bibliography  Save this article

Variable Selection in Multivariate Functional Linear Regression

Author

Listed:
  • Chi-Kuang Yeh

    (University of Waterloo)

  • Peijun Sang

    (University of Waterloo)

Abstract

Multivariate functional linear regression is commonly adopted to model the effects of several function-valued covariates on a scalar response. To select functional covariates with a time-varying effect, we develop a framework based on the reproducing kernel Hilbert space (RKHS). In particular, each coefficient function is assumed to reside in this RKHS and an RKHS norm is chosen as the penalty function in the regularized empirical risk function. This special penalty term enables us to achieve sparsity and smoothness when fitting multivariate functional linear models. Moreover, simulation studies demonstrate that the proposed estimator compares favorably with some traditional methods in variable selection, function estimation and prediction in finite samples. Finally, we apply the proposed framework to two real examples.

Suggested Citation

  • Chi-Kuang Yeh & Peijun Sang, 2025. "Variable Selection in Multivariate Functional Linear Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(1), pages 17-34, April.
    • Handle: RePEc:spr:stabio:v:17:y:2025:i:1:d:10.1007_s12561-023-09373-x
    DOI: 10.1007/s12561-023-09373-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-023-09373-x
    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/s12561-023-09373-x?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:stabio:v:17:y:2025:i:1:d:10.1007_s12561-023-09373-x. 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.