Please use this identifier to cite or link to this item: https://doi.org/10.1080/03610926.2011.563012
DC FieldValue
dc.titleA generalized chain binomial model with aggregated data
dc.contributor.authorXu, Y.
dc.contributor.authorYip, P.S.F.
dc.contributor.authorHuggins, R.M.
dc.date.accessioned2014-11-26T09:03:24Z
dc.date.available2014-11-26T09:03:24Z
dc.date.issued2012
dc.identifier.citationXu, Y., Yip, P.S.F., Huggins, R.M. (2012). A generalized chain binomial model with aggregated data. Communications in Statistics - Theory and Methods 41 (18) : 3325-3338. ScholarBank@NUS Repository. https://doi.org/10.1080/03610926.2011.563012
dc.identifier.issn03610926
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/110470
dc.description.abstractIn large cohort studies it can be impractical to report individual data that only summary or aggregated data are available. Using aggregated data from Bernoulli trials is expected to result in overdispersion so that a quasi-binomial approach would seem feasible. We show that when applied to aggregated data arising from cohorts of individuals according to a chain binomial model, the quasi-binomial model results in biased estimates. We propose an alternate calibration estimator and demonstrate its improved performance by simulations. The calibration method is then applied to model the probability of leaving a personal emergency link service in Hong Kong. Copyright © Taylor & Francis Group, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/03610926.2011.563012
dc.sourceScopus
dc.subjectAggregated data
dc.subjectCalibration approximation
dc.subjectChain binomial
dc.subjectPersonal emergency link service
dc.typeArticle
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.description.doi10.1080/03610926.2011.563012
dc.description.sourcetitleCommunications in Statistics - Theory and Methods
dc.description.volume41
dc.description.issue18
dc.description.page3325-3338
dc.description.codenCSTMD
dc.identifier.isiut000308465400002
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