Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/431
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dc.contributor.authorSHKEDY, Ziv-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorBeutels, Phillipe-
dc.contributor.authorVan Damme, Pierre-
dc.date.accessioned2004-10-29T09:23:56Z-
dc.date.available2004-10-29T09:23:56Z-
dc.date.issued2003-
dc.identifier.citationJournal of the Royal Statistical Society: series C: applied statistics, 52(4). p. 469-485-
dc.identifier.issn0035-9254-
dc.identifier.urihttp://hdl.handle.net/1942/431-
dc.description.abstractOn the basis of serological data from prevalence studies of rubella, mumps and hepatitis A, the paper describes a flexible local maximum likelihood method for the estimation of the rate at which susceptible individuals acquire infection at different ages. In contrast with parametric models that have been used before in the literature, the local polynomial likelihood method allows this age-dependent force of infection to be modelled without making any assumptions about the parametric structure. Moreover, this method allows for simultaneous nonparametric estimation of age-specific incidence and prevalence. Unconstrained models may lead to negative estimates for the force of infection at certain ages. To overcome this problem and to guarantee maximal flexibility, the local smoother can be constrained to be monotone. It turns out that different parametric and nonparametric estimates of the force of infection can exhibit considerably different qualitative features like location and the number of maxima, emphasizing the importance of a well-chosen flexible statistical model.-
dc.description.sponsorshipThe first three authors gratefully acknowledge support from the Belgian interuniversity attraction pool network ‘Statistical techniques and modeling for complex substantive questions with complex data’. We gratefully acknowledge financial support from the Flemish Fund for Scientific Research (grant G.0023.01N).-
dc.format.extent466882 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherBLACKWELL PUBLISHING-
dc.rights(C) 2003 Royal Statistical Society-
dc.subjectInfectious diseases-
dc.subject.otherforce of infection; fractional polynomial; incidence; isotonic regression; local polynomial; prevalence data; smoothing parameter-
dc.titleModeling forces of infection using monotone local polynomials-
dc.typeJournal Contribution-
dc.identifier.epage485-
dc.identifier.issue4-
dc.identifier.spage469-
dc.identifier.volume52-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1111/1467-9876.00418-
dc.identifier.isi000186031800007-
item.validationecoom 2004-
item.accessRightsOpen Access-
item.fullcitationSHKEDY, Ziv; AERTS, Marc; MOLENBERGHS, Geert; Beutels, Phillipe & Van Damme, Pierre (2003) Modeling forces of infection using monotone local polynomials. In: Journal of the Royal Statistical Society: series C: applied statistics, 52(4). p. 469-485.-
item.fulltextWith Fulltext-
item.contributorSHKEDY, Ziv-
item.contributorAERTS, Marc-
item.contributorMOLENBERGHS, Geert-
item.contributorBeutels, Phillipe-
item.contributorVan Damme, Pierre-
crisitem.journal.issn0035-9254-
crisitem.journal.eissn1467-9876-
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