IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v191y2024ics016794732300186x.html
   My bibliography  Save this article

Integrating machine learning and Bayesian nonparametrics for flexible modeling of point pattern data

Author

Listed:
  • Heaton, Matthew J.
  • Dahl, Benjamin K.
  • Dayley, Caleb
  • Warr, Richard L.
  • White, Philip

Abstract

Two common approaches to analyze point pattern (location-only) data are mixture models and log-Gaussian Cox processes. The former provides a flexible model for the intensity surface at the expense of no covariate effect estimates while the latter estimates covariate effects at the expense of computation. A bridge is built between these two methods that leverages the strengths of both approaches. Namely, Bayesian nonparametrics are first used to flexibly model the intensity surface. The posterior draws of the fitted intensity surface are then transformed into the equivalent representation under the log-Gaussian Cox process approach. Using principles of machine learning, estimates of covariate effects are obtained. The proposed two-step approach results in accurate estimates of parameters, with proper uncertainty quantification, which is illustrated with real and simulated examples.

Suggested Citation

  • Heaton, Matthew J. & Dahl, Benjamin K. & Dayley, Caleb & Warr, Richard L. & White, Philip, 2024. "Integrating machine learning and Bayesian nonparametrics for flexible modeling of point pattern data," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:csdana:v:191:y:2024:i:c:s016794732300186x
    DOI: 10.1016/j.csda.2023.107875
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794732300186X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2023.107875?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:191:y:2024:i:c:s016794732300186x. 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.