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dc.contributor.advisorRue, Haavard
dc.contributor.authorAlahmadi, Hanan H.
dc.date.accessioned2021-11-18T13:51:27Z
dc.date.available2021-11-18T13:51:27Z
dc.date.issued2021-11-16
dc.identifier.citationAlahmadi, H. H. (2021). Joint Quantile Disease Mapping for Areal Data. KAUST Research Repository. https://doi.org/10.25781/KAUST-3R42B
dc.identifier.doi10.25781/KAUST-3R42B
dc.identifier.urihttp://hdl.handle.net/10754/673677
dc.description.abstractThe statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping has usually focused on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease. In the proposed joint quantile model, the key idea is to link the diseases with different quantiles and estimate their dependence instead of connecting their means. The various components of this formulation are modeled by using the latent Gaussian model, and the parameters were estimated via R-INLA. Finally, we illustrate the model by analyzing the malaria and G6PD deficiency incidences in 21 African countries.
dc.language.isoen
dc.subjectdisease mapping
dc.subjectquantile
dc.subjectINLA
dc.subjectBayesian
dc.subjectareal data
dc.subjecttext
dc.titleJoint Quantile Disease Mapping for Areal Data
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.rights.embargodate2022-11-16
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberLaleg-Kirati, Taous-Meriem
dc.contributor.committeememberMoraga, Paula
dc.contributor.committeememberSilva, Giovani
thesis.degree.disciplineStatistics
thesis.degree.nameMaster of Science
dc.rights.accessrightsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2022-11-16.
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