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

Marianna Siino, Giada Adelfio, Francisco J. Rodríguez-Cortés, Marianna Siino, Jorge Mateu

Risultato della ricerca: Articlepeer review

2 Citazioni (Scopus)


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.
Lingua originaleEnglish
Numero di pagine17
Stato di pubblicazionePublished - 2020

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.2600.2613???
  • ???subjectarea.asjc.2300.2302???


Entra nei temi di ricerca di 'Spatio-temporal classification in point patterns under the presence of clutter'. Insieme formano una fingerprint unica.

Cita questo