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dc.contributor.authorWimmer, Courtney
dc.contributor.authorDias, James
dc.date.accessioned2021-06-14T18:32:39Z
dc.date.available2021-06-14T18:32:39Z
dc.date.issued2009-04
dc.identifier.urien
dc.identifier.urihttp://hdl.handle.net/10675.2/624110
dc.description.abstractConfidence interval estimation for a binomial proportion is a long debated topic, resulting in a wide range of exact and approximate methods. Many of these methods perform quite poorly when the number of observed successes in a sample of size n is zero. In this case, the main objective of the investigator is usually to obtain an upper bound, i.e., the upper limit of a onesided confidence interval. Traditional notions of expected interval length and coverage probability are not applicable in this situation. In this paper we use observed interval length and p-confidence to evaluate nine confidence interval methods for a binomial proportion. We also consider approximate sample sizes needed to achieve various upper bounds near the zero boundary. We show that many popular approximation methods perform poorly based on these criteria and conclude that the-exact method has superior performance in terms of interval length and p-confidence.en_US
dc.language.isoen_USen_US
dc.publisherAugusta Universityen_US
dc.rightsCopyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law.en_US
dc.subjectApproximate Methodsen_US
dc.subjectBinomal Parameteren_US
dc.subjectExact Methodsen_US
dc.titleConfidence interval estimation for a binomial proportion when no successes are observeden_US
dc.typeThesisen_US
dc.contributor.departmentMedical College of Georgiaen_US
dc.description.advisorLooney, Stephen
dc.description.advisorWaller, Jennifer
dc.description.committeeRoberts, Steven
dc.description.committeeRussell, Carl
dc.description.committeeDias, James
dc.description.degreeMaster of Scienceen_US
dc.embargoen
refterms.dateFOA2021-06-14T18:32:40Z


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