IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v31y2020i2ne2599.html
   My bibliography  Save this article

Spatio‐temporal classification in point patterns under the presence of clutter

Author

Listed:
  • Marianna Siino
  • Francisco J. Rodríguez‐Cortés
  • Jorge Mateu
  • Giada Adelfio

Abstract

We consider the problem of detection of features in the presence of clutter for spatio‐temporal point patterns. In previous studies, related to the spatial context, Kth nearest‐neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation‐maximization algorithm. This paper extends this methodology to the spatio‐temporal context by considering the properties of the spatio‐temporal Kth nearest‐neighbor distances. For this purpose, we make use of a couple of spatio‐temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such Kth nearest‐neighbor distances and present an intensive simulation study together with an application to earthquakes.

Suggested Citation

  • Marianna Siino & Francisco J. Rodríguez‐Cortés & Jorge Mateu & Giada Adelfio, 2020. "Spatio‐temporal classification in point patterns under the presence of clutter," Environmetrics, John Wiley & Sons, Ltd., vol. 31(2), March.
    • Handle: RePEc:wly:envmet:v:31:y:2020:i:2:n:e2599
    DOI: 10.1002/env.2599
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2599
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2599?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jieying Jiao & Guanyu Hu & Jun Yan, 2021. "Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.

    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:wly:envmet:v:31:y:2020:i:2:n:e2599. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.