Spatio‐temporal classification in point patterns under the presence of clutter.
Impacto
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONEste recurso está restringido
https://doi.org/10.1002/env.2599 |
Metadatos
Título
Spatio‐temporal classification in point patterns under the presence of clutter.Fecha de publicación
2019-08-23Editor
WileyCita bibliográfica
SIINO, Marianna; RODRÍGUEZ CORTÉS, Francisco Javier; MATEU, Jorge; ADELFIO, Giada (2019). Spatio‐temporal classification in point patterns under the presence of clutter. Environmetrics, online 23/8/2019Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/full/10.1002/env.2599Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We consider the problem of detection of features in the presence of clutter forspatio-temporal point patterns. In previous studies, related to the spatial context,Kth nearest-neighbor distances to classify points ... [+]
We consider the problem of detection of features in the presence of clutter forspatio-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 usingan expectation-maximization algorithm. This paper extends this methodology tothe spatio-temporal context by considering the properties of the spatio-temporalKth nearest-neighbor distances. For this purpose, we make use of a couple ofspatio-temporal distances, which are based on the Euclidean and the maxi-mum norms. We show close forms for the probability distributions of suchKthnearest-neighbor distances and present an intensive simulation study togetherwith an application to earthquakes. [-]
Publicado en
Environmetrics (2019), online versionProyecto de investigación
1) National grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN‐2015 program, “Complex space‐time modeling and functional analysis for probabilistic forecast of seismic events.”; 2) Universidad Nacional de Colombia, Hermes projects 44993.Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
Aparece en las colecciones
- INIT_Articles [752]
- MAT_Articles [765]