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  4. Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models
Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4057
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dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.date.accessioned2022-08-19T18:58:41Z-
dc.date.available2022-08-19T18:58:41Z-
dc.date.issued2012-
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4057-
dc.format.extentp. 284-303pt_BR
dc.format.mediumDigitalpt_BR
dc.language.isoInglêspt_BR
dc.publisherInstitute of Mathematical Statisticspt_BR
dc.relation.ispartofThe Annals of Applied Statisticspt_BR
dc.relation.urihttps://projecteuclid.org/journals/annals-of-applied-statistics/volume-6/issue-1/Measuring-the-vulnerability-of-the-Uruguayan-population-to-vector-borne/10.1214/11-AOAS497.full?tab=ArticleLinkpt_BR
dc.titleMeasuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor modelspt_BR
dc.typeArtigo Científicopt_BR
dc.description.otherWe propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators and geographical location in the country. Our index is based on a new class of spatially hierarchical factor models that explicitly account for the different levels of hierarchy in the country, such as census tracts within the city level, and cities in the country level. We compare our approach with that obtained when data are aggregated in the city level. We show that our proposal outperforms current and standard approaches, which fail to properly account for discrepancies in the region sizes, for example, number of census tracts. We also show that data aggregation can seriously affect the estimation of the cities vulnerability rankings under benchmark models.pt_BR
dc.subject.cnpqCiências Sociais Aplicadaspt_BR
dc.subject.keywordsAreal datapt_BR
dc.subject.keywordsBayesian inferencept_BR
dc.subject.keywordsmodel comparisonpt_BR
dc.subject.keywordsspatial interpolationpt_BR
dc.subject.keywordsspatial smoothingpt_BR
dc.identifier.doi10.1214/11-AOAS497pt_BR
dc.identifier.issue1pt_BR
dc.identifier.volume6pt_BR
dc.contributor.autorLopes, Hedibert Freitas-
dc.contributor.autorSchmidt, Alexandra M.-
dc.contributor.autorSalazar, Esther-
dc.contributor.autorGómez, Mariana-
dc.contributor.autorAchkar, Marcel-
dc.coverage.paisNão Informadopt_BR
dc.coverage.cidadeNão informadopt_BR
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