IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v185y2023ics0167947323000774.html
   My bibliography  Save this article

Functional clustering methods for binary longitudinal data with temporal heterogeneity

Author

Listed:
  • Sohn, Jinwon
  • Jeong, Seonghyun
  • Cho, Young Min
  • Park, Taeyoung

Abstract

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.

Suggested Citation

  • Sohn, Jinwon & Jeong, Seonghyun & Cho, Young Min & Park, Taeyoung, 2023. "Functional clustering methods for binary longitudinal data with temporal heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:csdana:v:185:y:2023:i:c:s0167947323000774
    DOI: 10.1016/j.csda.2023.107766
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947323000774
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2023.107766?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:csdana:v:185:y:2023:i:c:s0167947323000774. 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/locate/csda .

    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.