IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v209y2025ics0047259x25000594.html
   My bibliography  Save this article

Deconvolution density estimation on Lie groups without auxiliary data

Author

Listed:
  • Jeon, Jeong Min

Abstract

In this paper, we study density estimation on a general Lie group when data contain measurement errors and the distribution of measurement error is unknown. We estimate the target density without additional observations, such as an observable random sample from the measurement error distribution or repeated measurements. To achieve this, we take a semiparametric approach assuming that the measurement error distribution belongs to a parametric family. We also discuss maximum likelihood estimation for the case where the target density is also parametric. We establish the identifiability of a measurement error model and derive various asymptotic properties for our estimators. The performance of our estimators is demonstrated via simulation studies.

Suggested Citation

  • Jeon, Jeong Min, 2025. "Deconvolution density estimation on Lie groups without auxiliary data," Journal of Multivariate Analysis, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:jmvana:v:209:y:2025:i:c:s0047259x25000594
    DOI: 10.1016/j.jmva.2025.105464
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X25000594
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2025.105464?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:eee:jmvana:v:209:y:2025:i:c:s0047259x25000594. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.