Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters.
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/1738 |
Resumo: | Background: Irregularly shape d 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 excessive freedom of the shape of clusters . Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under -populated disconnection nodes in candid ate clusters , the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function , the most geographicall y meaning ful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is use d. In this pa per we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function . We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas’ disease in puerperal women in Minas Gerais state, Brazil. Conclusions : We show that, compared to the other single-objective algorithm s, multi- objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi objective non-connectivity scan is faster and better suited for the detect ion of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters . |
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Cançado, André Luiz FernandesDuarte, Anderson RibeiroDuczmal, Luiz HenriqueFerreira, Sabino JoséFonseca, Carlos M.Gontijo, Eliane Dias2012-10-24T17:50:09Z2012-10-24T17:50:09Z2010CANÇADO, A. L. F. et al. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. International Journal of Health Geographics, v.9, n. 55, p. 1-17, 2010. Disponível em: http://www.ij-healthgeographics.com/content/pdf/1476-072X-9-55.pdf. Acesso em: 24/10/20121476072Xhttp://www.repositorio.ufop.br/handle/123456789/1738Background: Irregularly shape d 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 excessive freedom of the shape of clusters . Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under -populated disconnection nodes in candid ate clusters , the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function , the most geographicall y meaning ful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is use d. In this pa per we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function . We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas’ disease in puerperal women in Minas Gerais state, Brazil. Conclusions : We show that, compared to the other single-objective algorithm s, multi- objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi objective non-connectivity scan is faster and better suited for the detect ion of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters .Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters.info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleAutores de artigos publicados no International Journal of Health Geographics são os detentores do copyright de seus artigos e concederam a qualquer terceiro o direito de usar, repoduzir ou disseminar o artigo. Fonte: International Journal of Health Geographics <http://www.ij-healthgeographics.com/about> Acesso em 01 Dez. 2013.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://www.repositorio.ufop.br/bitstream/123456789/1738/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD55ORIGINALARTIGO_PenalizedLikelihoodObjective.pdfARTIGO_PenalizedLikelihoodObjective.pdfapplication/pdf1515002http://www.repositorio.ufop.br/bitstream/123456789/1738/1/ARTIGO_PenalizedLikelihoodObjective.pdf192ea92ab10dce5aebab14d3e799af50MD51123456789/17382016-08-04 09:35:29.429oai:localhost: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Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332016-08-04T13:35:29Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.pt_BR.fl_str_mv |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
title |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
spellingShingle |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. Cançado, André Luiz Fernandes |
title_short |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
title_full |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
title_fullStr |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
title_full_unstemmed |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
title_sort |
Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. |
author |
Cançado, André Luiz Fernandes |
author_facet |
Cançado, André Luiz Fernandes Duarte, Anderson Ribeiro Duczmal, Luiz Henrique Ferreira, Sabino José Fonseca, Carlos M. Gontijo, Eliane Dias |
author_role |
author |
author2 |
Duarte, Anderson Ribeiro Duczmal, Luiz Henrique Ferreira, Sabino José Fonseca, Carlos M. Gontijo, Eliane Dias |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Cançado, André Luiz Fernandes Duarte, Anderson Ribeiro Duczmal, Luiz Henrique Ferreira, Sabino José Fonseca, Carlos M. Gontijo, Eliane Dias |
description |
Background: Irregularly shape d 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 excessive freedom of the shape of clusters . Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under -populated disconnection nodes in candid ate clusters , the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function , the most geographicall y meaning ful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is use d. In this pa per we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function . We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas’ disease in puerperal women in Minas Gerais state, Brazil. Conclusions : We show that, compared to the other single-objective algorithm s, multi- objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi objective non-connectivity scan is faster and better suited for the detect ion of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters . |
publishDate |
2010 |
dc.date.issued.fl_str_mv |
2010 |
dc.date.accessioned.fl_str_mv |
2012-10-24T17:50:09Z |
dc.date.available.fl_str_mv |
2012-10-24T17:50:09Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CANÇADO, A. L. F. et al. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. International Journal of Health Geographics, v.9, n. 55, p. 1-17, 2010. Disponível em: http://www.ij-healthgeographics.com/content/pdf/1476-072X-9-55.pdf. Acesso em: 24/10/2012 |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.ufop.br/handle/123456789/1738 |
dc.identifier.issn.none.fl_str_mv |
1476072X |
identifier_str_mv |
CANÇADO, A. L. F. et al. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. International Journal of Health Geographics, v.9, n. 55, p. 1-17, 2010. Disponível em: http://www.ij-healthgeographics.com/content/pdf/1476-072X-9-55.pdf. Acesso em: 24/10/2012 1476072X |
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http://www.repositorio.ufop.br/handle/123456789/1738 |
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