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

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

Risultato della ricerca: Article

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.
Lingua originaleEnglish
Numero di pagine17
RivistaEnvironmetrics
Stato di pubblicazionePublished - 2019

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Clutter
Mixture of Distributions
earthquake
Maximum Norm
methodology
Expectation-maximization Algorithm
Earthquake
simulation
Euclidean
Probability Distribution
Classify
Simulation Study
distribution
Methodology
Context
norm
detection
parameter

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Ecological Modelling

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Spatio‐temporal classification in point patterns under the presence of clutter. / Adelfio, Giada; Siino, Marianna; Rodríguez-Cortés, Francisco J.; Siino, Marianna; Mateu, Jorge.

In: Environmetrics, 2019.

Risultato della ricerca: Article

Adelfio, Giada ; Siino, Marianna ; Rodríguez-Cortés, Francisco J. ; Siino, Marianna ; Mateu, Jorge. / Spatio‐temporal classification in point patterns under the presence of clutter. In: Environmetrics. 2019.
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AU - Mateu, Jorge

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AB - 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.

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