Archivio istituzionale della ricerca dell'Università degli Studi di Palermo
IRIS è il sistema di gestione integrata dei dati della ricerca (persone, pubblicazioni, progetti, attività) adottato dall’Università di Palermo, e ha lo scopo di raccogliere, conservare, documentare, e diffondere ad accesso aperto le informazioni relative alla produzione scientifica degli autori afferenti all’Ateneo.
EnglishWe 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.
Siino M, Rodríguez‐Cortés FJ, Mateu J, & Adelfio G (2020). Spatio-temporal classification in point patterns under the presence of clutter. ENVIRONMETRICS, 31(2) [10.1002/env.2599].
| Data di pubblicazione: | 2020 | |
| Titolo: | Spatio-temporal classification in point patterns under the presence of clutter | |
| Autori: | ADELFIO, Giada (Corresponding) | |
| Citazione: | Siino M, Rodríguez‐Cortés FJ, Mateu J, & Adelfio G (2020). Spatio-temporal classification in point patterns under the presence of clutter. ENVIRONMETRICS, 31(2) [10.1002/env.2599]. | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1002/env.2599 | |
| 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. | |
| Settore Scientifico Disciplinare: | Settore SECS-S/01 - Statistica | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Siino_et_al-2019-Environmetrics.pdf | Versione Editoriale | Administrator Richiedi una copia | ||
| GiadaAdelfio _Paper_submitted.pdf | Pre-print | Open Access Visualizza/Apri |
http://hdl.handle.net/10447/367404
Copyright © 2022