IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v51y2024i2p447-484.html
   My bibliography  Save this article

Kernel mean embedding of probability measures and its applications to functional data analysis

Author

Listed:
  • Saeed Hayati
  • Kenji Fukumizu
  • Afshin Parvardeh

Abstract

This study intends to introduce kernel mean embedding of probability measures over infinite‐dimensional separable Hilbert spaces induced by functional response statistical models. The embedded function represents the concentration of probability measures in small open neighborhoods, which identifies a pseudo‐likelihood and fosters a rich framework for statistical inference. Utilizing Maximum Mean Discrepancy, we devise new tests in functional response models. The performance of new derived tests is evaluated against competitors in three major problems in functional data analysis including Function‐on‐Scalar regression, functional one‐way ANOVA, and equality of covariance operators.

Suggested Citation

  • Saeed Hayati & Kenji Fukumizu & Afshin Parvardeh, 2024. "Kernel mean embedding of probability measures and its applications to functional data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 447-484, June.
    • Handle: RePEc:bla:scjsta:v:51:y:2024:i:2:p:447-484
    DOI: 10.1111/sjos.12691
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/sjos.12691
    Download Restriction: no
    File URL: https://libkey.io/10.1111/sjos.12691?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
    ---><---

    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:scjsta:v:51:y:2024:i:2:p:447-484. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.