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

Research output: Chapter in Book/Report/Conference proceedingConference 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.
Original languageEnglish
Title of host publicationSmart Statistics for Smart Applications Book of short paper
Number of pages8
Publication statusPublished - 2019

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