Classification of spatio-temporal point pattern inthe presence of clutter using K-th nearestneighbour distances

Risultato della ricerca: Conference contribution

Abstract

In a point process spatio-temporal framework, we consider the problemof features detection in the presence of clutters. We extend the methodology of Byersand Raftery (1998) to the spatio-temporal context by considering the propertiesof the K-th nearest-neighbour distances. We make use of the spatio-temporal distancebased on the Euclidean norm where the temporal term is properly weighted.We show the form of the probability distributions of such K-th nearest-neighbourdistance. A mixture distribution, whose parameters are estimated with an EM algorithm,is used to classify points into clutters or features. We assess the performanceof the proposed approach with a simulation study, together with an application toearthquakes.
Lingua originaleEnglish
Titolo della pubblicazione ospiteSmart Statistics for Smart Applications Book of short paper
Numero di pagine8
Stato di pubblicazionePublished - 2019

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Classification of spatio-temporal point pattern inthe presence of clutter using K-th nearestneighbour distances. / Siino, Marianna; Adelfio, Giada.

Smart Statistics for Smart Applications Book of short paper. 2019.

Risultato della ricerca: Conference contribution

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title = "Classification of spatio-temporal point pattern inthe presence of clutter using K-th nearestneighbour distances",
abstract = "In a point process spatio-temporal framework, we consider the problemof features detection in the presence of clutters. We extend the methodology of Byersand Raftery (1998) to the spatio-temporal context by considering the propertiesof the K-th nearest-neighbour distances. We make use of the spatio-temporal distancebased on the Euclidean norm where the temporal term is properly weighted.We show the form of the probability distributions of such K-th nearest-neighbourdistance. A mixture distribution, whose parameters are estimated with an EM algorithm,is used to classify points into clutters or features. We assess the performanceof the proposed approach with a simulation study, together with an application toearthquakes.",
author = "Marianna Siino and Giada Adelfio",
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AU - Siino, Marianna

AU - Adelfio, Giada

PY - 2019

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N2 - In a point process spatio-temporal framework, we consider the problemof features detection in the presence of clutters. We extend the methodology of Byersand Raftery (1998) to the spatio-temporal context by considering the propertiesof the K-th nearest-neighbour distances. We make use of the spatio-temporal distancebased on the Euclidean norm where the temporal term is properly weighted.We show the form of the probability distributions of such K-th nearest-neighbourdistance. A mixture distribution, whose parameters are estimated with an EM algorithm,is used to classify points into clutters or features. We assess the performanceof the proposed approach with a simulation study, together with an application toearthquakes.

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

UR - http://hdl.handle.net/10447/371370

M3 - Conference contribution

BT - Smart Statistics for Smart Applications Book of short paper

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