IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i5p1133-1154.html
   My bibliography  Save this article

Optimal wavelet estimators of the heteroscedastic pointspread effects and Gauss white noises model

Author

Listed:
  • Jinru Wang
  • Wenhui Shi
  • Xiaochen Zeng

Abstract

The problem of estimating a function’s derivative arises in many scientific settings, from medical imaging to astronomy. In this paper, we consider a hard thresholding wavelet approach to certain blind deconvolution problem based on the heteroscedastic data. Motivated by Delaigle & Meister’s work, the adaptive wavelet estimators are proposed and their asymptotic properties are investigated. It is shown that the derivative estimators for the blind deconvolution model are spatially adaptive and attain the optimal rate of convergence up to a logarithmic factor over a range of Besov classes. Our theorems generalize the results of Cai and Navarro et al. in some sense.

Suggested Citation

  • Jinru Wang & Wenhui Shi & Xiaochen Zeng, 2022. "Optimal wavelet estimators of the heteroscedastic pointspread effects and Gauss white noises model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(5), pages 1133-1154, March.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:5:p:1133-1154
    DOI: 10.1080/03610926.2020.1862874
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1862874
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1862874?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:lstaxx:v:51:y:2022:i:5:p:1133-1154. 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/lsta .

    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.