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Spatial generalised linear mixed models based on distances
dc.contributor.author | Melo, Oscar O. | |
dc.contributor.author | Mateu, Jorge | |
dc.contributor.author | Melo, Carlos E. | |
dc.date.accessioned | 2017-01-13T10:38:29Z | |
dc.date.available | 2017-01-13T10:38:29Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 0962-2802 | |
dc.identifier.issn | 1477-0334 | |
dc.identifier.uri | http://hdl.handle.net/10234/165340 | |
dc.description.abstract | Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. | ca_CA |
dc.description.sponsorShip | This work is partially funded and supported by grant MTM2010-14961 from the Spanish Ministry of Science and Education, Carolina Foundation, Applied Statistics in Experimental Research, Industry and Biotechnology (National University of Colombia), and Core Spatial Data Research (Faculty of Engineering, District University Francisco Jose´ de Caldas). | ca_CA |
dc.format.extent | 23 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Sage Publications | ca_CA |
dc.relation.isPartOf | Statistical Methods in Medical Research 2016, Vol. 25(5) 2138–2160 | ca_CA |
dc.rights | © The Author(s) 2013. Reprints and permissions: sagepub.co.uk/journalsPermissions.nav | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | distance-based methods | ca_CA |
dc.subject | epidemiological study | ca_CA |
dc.subject | Markov chain Monte Carlo | ca_CA |
dc.subject | spatial generalised linear mixed models | ca_CA |
dc.subject | spatial interpolation | ca_CA |
dc.title | Spatial generalised linear mixed models based on distances | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1177/0962280213515792 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | http://journals.sagepub.com/doi/full/10.1177/0962280213515792 | ca_CA |
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