Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Background: Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the exc...

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Detalhes bibliográficos
Autor principal: Cancado, Andre L. F. (author)
Outros Autores: Duarte, Anderson R. (author), Duczmal, Luiz H. (author), Ferreira, Sabino J. (author), Fonseca, Carlos M. (author), Gontijo, Eliane C. D. M. (author)
Formato: article
Idioma:eng
Publicado em: 2018
Assuntos:
Shaped Disease Clusters
Hotspot Detection
Attainment Function
Genetic Algorithm
Statistics
Surveillance
Optimization
Performance
Optimizers
Tests
Texto completo:http://hdl.handle.net/10400.1/11883
País:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/11883

Texto Completo

http://hdl.handle.net/10400.1/11883

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