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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Bibliographic Details
Main Author: Cancado, Andre L. F. (author)
Other Authors: Duarte, Anderson R. (author), Duczmal, Luiz H. (author), Ferreira, Sabino J. (author), Fonseca, Carlos M. (author), Gontijo, Eliane C. D. M. (author)
Format: article
Language:eng
Published: 2018
Subjects:
Shaped Disease Clusters
Hotspot Detection
Attainment Function
Genetic Algorithm
Statistics
Surveillance
Optimization
Performance
Optimizers
Tests
Online Access:http://hdl.handle.net/10400.1/11883
Country:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/11883

Internet

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

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