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dc.contributor.advisorYoung, Dean M.
dc.contributor.authorRahardja, Dewi Gabriela.
dc.contributor.otherBaylor University. Dept. of Statistical Sciences.en
dc.date.accessioned2011-01-05T19:44:19Z
dc.date.available2011-01-05T19:44:19Z
dc.date.copyright2010-12
dc.date.issued2011-01-05T19:44:19Z
dc.identifier.urihttp://hdl.handle.net/2104/8095
dc.descriptionIncludes bibliographical references (p. ).en
dc.description.abstractWe consider the problem of point and interval estimation for the risk ratio using double sampling with two-sample misclassified binary data. For such data, it is well-known that the actual data model is unidentifiable. To achieve model identifiability, then, we obtain additional data via a double-sampling scheme. For the Bayesian paradigm, we devise a parametric, straight-forward algorithm for sampling from the joint posterior density for the parameters, given the data. We then obtain Bayesian point and interval estimators of the risk ratio of two-proportion parameters. We illustrate our algorithm using a real data example and conduct two Monte Carlo simulation studies to demonstrate that both the point and interval estimators perform well. Additionally, we derive three likelihood-based confidence intervals (CIs) for the risk ratio. Specifically, we first obtain closed-form maximum likelihood estimators (MLEs) for all parameters. We then derive three CIs for the risk ratio: a naive Wald interval, a modified Wald interval, and a Fieller-type interval. For illustration purposes, we apply the three CIs to a real data example. We also perform various Monte Carlo simulation studies to assess and compare the coverage probabilities and average lengths of the three CIs. A modified Wald CI performs the best of the three CIs and has near-nominal coverage probabilities.en
dc.description.statementofresponsibilityby Dewi Gabriela Rahardja.en
dc.format.extent99363 bytes
dc.format.extent406309 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectMisclassification.en
dc.subjectBinomial data.en
dc.subjectDouble sampling.en
dc.subjectRisk ratio.en
dc.subjectInterval estimation.en
dc.titleBayesian and likelihood-based interval estimation for the risk ratio using double sampling with misclassified binomial data.en
dc.typeThesisen
dc.description.degreePh.D.en
dc.rights.accessrightsWorldwide access.en
dc.rights.accessrightsAccess changed 3/18/13.
dc.contributor.departmentStatistical Sciences.en


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