Spatio‐temporal classification in point patterns under the presence of clutter.
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Other documents of the author: SIINO, MARIANNA; Rodríguez-Cortés, Francisco Javier; Mateu, Jorge; adelfio, giada
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1002/env.2599 |
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Title
Spatio‐temporal classification in point patterns under the presence of clutter.Date
2019-08-23Publisher
WileyBibliographic citation
SIINO, Marianna; RODRÍGUEZ CORTÉS, Francisco Javier; MATEU, Jorge; ADELFIO, Giada (2019). Spatio‐temporal classification in point patterns under the presence of clutter. Environmetrics, online 23/8/2019Type
info:eu-repo/semantics/articlePublisher version
https://onlinelibrary.wiley.com/doi/full/10.1002/env.2599Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Environmetrics (2019), online versionInvestigation project
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.Rights
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info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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