In a point process spatio-temporal framework, we consider the problem of features detection in the presence of clutters. We extend the methodology of Byers and Raftery (1998) to the spatio-temporal context by considering the properties of the K-th nearest-neighbour distances. We make use of the spatio-temporal distance based on the Euclidean norm where the temporal term is properly weighted. We show the form of the probability distributions of such K-th nearest-neighbour distance. A mixture distribution, whose parameters are estimated with an EM algorithm, is used to classify points into clutters or features. We assess the performance of the proposed approach with a simulation study, together with an application to earthquakes.

Siino Marianna, F.J.R. (2019). Classification of spatio-temporal point pattern in the presence of clutter using K-th nearest neighbour distances. In Smart Statistics for Smart Applications Book of short paper.

Classification of spatio-temporal point pattern in the presence of clutter using K-th nearest neighbour distances

Siino Marianna
;
Giada Adelfio
2019-01-01

Abstract

In a point process spatio-temporal framework, we consider the problem of features detection in the presence of clutters. We extend the methodology of Byers and Raftery (1998) to the spatio-temporal context by considering the properties of the K-th nearest-neighbour distances. We make use of the spatio-temporal distance based on the Euclidean norm where the temporal term is properly weighted. We show the form of the probability distributions of such K-th nearest-neighbour distance. A mixture distribution, whose parameters are estimated with an EM algorithm, is used to classify points into clutters or features. We assess the performance of the proposed approach with a simulation study, together with an application to earthquakes.
Settore SECS-S/01 - Statistica
Siino Marianna, F.J.R. (2019). Classification of spatio-temporal point pattern in the presence of clutter using K-th nearest neighbour distances. In Smart Statistics for Smart Applications Book of short paper.
File in questo prodotto:
File Dimensione Formato  
author.pdf

accesso aperto

Tipologia: Pre-print
Dimensione 426.17 kB
Formato Adobe PDF
426.17 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/371370
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact