IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v34y2022i4p917-939.html
   My bibliography  Save this article

A scalable quasi-Newton estimation algorithm for dynamic generalised linear models

Author

Listed:
  • Guangbao Guo
  • Guoqi Qian
  • Lixing Zhu

Abstract

This research develops a scalable computing method based on quasi-Newton algorithm for several estimation problems in dynamic generalised linear models (DGLMs). The new method is developed by applying the principle of maximising a pointwise penalised quasi-likelihood (PPQ) for a DGLM for observed data often of massive size. Statistical and computational challenges involved in this development have been effectively tackled by exploiting the specific block structure and sparsity involved in the underlying projection matrix. The obtained maximum PPQ estimator of the state vector in the DGLM has been shown to be consistent and asymptotically normal under regularity conditions. Numerical studies and real data applications are conducted to assess the performance of the developed method.

Suggested Citation

  • Guangbao Guo & Guoqi Qian & Lixing Zhu, 2022. "A scalable quasi-Newton estimation algorithm for dynamic generalised linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(4), pages 917-939, October.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:4:p:917-939
    DOI: 10.1080/10485252.2022.2085263
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2022.2085263
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2022.2085263?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:gnstxx:v:34:y:2022:i:4:p:917-939. 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/GNST20 .

    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.