IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-46161-4_22.html
   My bibliography  Save this book chapter

Simultaneous Multiple Change Points Estimation in Generalized Linear Models

In: Contemporary Experimental Design, Multivariate Analysis and Data Mining

Author

Listed:
  • Xiaoying Sun

    (York University)

  • Yuehua Wu

    (York University)

Abstract

In this paper, the problem of multiple change points estimation is considered for generalized linear models, in which both the number of change-points and their locations are unknown. The proposed method is to first partition the data sequence into segments to construct a new design matrix, secondly convert the multiple change points estimation problem into a variable selection problem, and then apply a regularized model selection technique and obtain the regression coefficient estimation. The consistency of the estimator is established regardless if there is a change point in which the number of coefficients can diverge as the sample size goes to infinity. An algorithm is provided to estimate the multiple change points. Simulation studies are conducted for the logistic and log-linear models. A real data application is also presented.

Suggested Citation

  • Xiaoying Sun & Yuehua Wu, 2020. "Simultaneous Multiple Change Points Estimation in Generalized Linear Models," Springer Books, in: Jianqing Fan & Jianxin Pan (ed.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, chapter 0, pages 341-356, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-46161-4_22
    DOI: 10.1007/978-3-030-46161-4_22
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-030-46161-4_22. 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.