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Modelling latent trends from spatio-temporally grouped data using composite link mixed models

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAyma Anza, Diego Armando
dc.contributor.authorDurbán, María
dc.contributor.authorLee, Dae-Jin
dc.contributor.authorVan de Kassteele, Jan
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2016-07-28T10:27:49Z
dc.date.accessioned2016-08-01T11:33:16Z
dc.date.available2016-08-01T11:33:16Z
dc.date.issued2016-07-25
dc.description.abstractEpidemiological data are frequently recorded at coarse spatio-temporal resolutions. The aggregation process is done for several reasons: to protect confidential patients' information, to compare with other datasets at a coarser resolution than the original, or to summarize data in a compact manner. However, we lose detailed patterns that follow the original data, which can be of interest for researchers and public health officials. In this paper we propose the use of the penalized composite link model (Eilers, 2007), together with its mixed model representation, to estimate the underlying trend behind grouped data at a finer spatio-temporal resolution. Also, this model allows the incorporation of fine-scale population into the estimation procedure. We assume the underlying trend is smooth across space and time. The mixed model representation enables the use of sophisticated algorithms such as the SAP algorithm of RodríguezÁlvarez et al. (2015) for fast estimation of the amount of smoothness. We illustrate our proposal with the analysis of data obtained during the largest outbreak of Q fever in the Netherlands.es
dc.description.sponsorshipThe first and the second authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness grants MTM2011-28285-C02-02 and MTM2014-52184-P. The third author acknowledges financial support from the Basque Government through the BERC 2014-2017 program and by the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation SEV-2013-0323.en
dc.format.mimetypeapplication/pdf
dc.identifier.issn2387-0303es
dc.identifier.urihttp://hdl.handle.net/10016/23448
dc.identifier.uxxiDT/0000001476es
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricses
dc.relation.ispartofseries16-07es
dc.relation.projectIDGobierno de España. MTM2011-28285-C02-02
dc.relation.projectIDGobierno de España. MTM2014-52184-Pes
dc.relation.projectIDGobierno de España. SEV-2013-0323es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherPenalized composite link modelses
dc.subject.otherQ fever incidencees
dc.subject.otherSAP algorithmes
dc.subject.otherSpatio-temporal disaggregationes
dc.titleModelling latent trends from spatio-temporally grouped data using composite link mixed modelses
dc.typeworking paper*
dc.type.hasVersionAO*
dspace.entity.typePublication
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