Classification of intensity functions of inhomogeneous point processes

Autor:	Fuentes-Santos, I., Borrajo, M. I., González-Manteiga, W.
Přispěvatelé:	Agencia Estatal de Investigación (España)
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Density of event locations Wasserstein metric k-means algorithm synthetic data functional data
Zdroj:	Digital.CSIC. Repositorio Institucional del CSIC instname
Popis:	5 pages, 1 figure, 2 tables.-- 10th International Workshop on Spatio-Temporal Modelling, Lleida (Spain), 1-3 June 2022 A common question when a given point process is observed in more than one population is whether those patterns share the same structure or they can be partitioned in a ceitain number of groups. A k-means algorithm could be used to classify the densities of event locations. However, the space of density functions does not fulfill Hilbert conditions, so specific measures should be adopted. In this work we propose some possibilities, and we compare their performance through a simulation study. Real data problems, such as the classification of COVID-19 infection curves can be addressed This work has been supported by Project PID2020-116587GB-I00 (AEI/FEDER, UE)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f3dc4b0db602ea74c3160aa9abb603e7 http://hdl.handle.net/10261/282869 Zobrazit plný text záznamu