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dc.contributor.authorHawkins, D. L.en
dc.contributor.authorEisenfeld, Jeromeen
dc.contributor.authorHan, C. P.en
dc.date.accessioned2010-06-14T16:43:46Zen
dc.date.available2010-06-14T16:43:46Zen
dc.date.issued1995en
dc.identifier.urihttp://hdl.handle.net/10106/2503en
dc.description.abstract**Please note that the full text is embargoed** ABSTRACT: Transition probabilities provide a convenient summary of changes in a categorical trait over time in a population. The difficulties of estimating such probabilities based on only aggregate data from repeated sampling are well known. We give here a method for augmenting aggregate data with haphazard recapture data, which can dramatically improve the estimation precision of transition probabilities. The method requires a rather high sampling fraction to provide sufficient numbers of recaptures. It is based on a generalized nonlinear least squares strategy which yields transition probability estimates preserving their natural parameter space, and which are asymptotically efficient. The asymptotic theory is given under finite population sampling assumptions which are typical in practice.en
dc.language.isoen_USen
dc.publisherUniversity of Texas at Arlingtonen
dc.relation.ispartofseriesTechnical Report;302en
dc.subjectAggregate dataen
dc.subjectSampling schemesen
dc.subjectRecapturesen
dc.subject.lcshMathematics Researchen
dc.subject.lcshStatisticsen
dc.titleEstimating Transition Probabilities from Aggregate Samples Augmented by Haphazard Recapturesen
dc.typeTechnical Reporten
dc.publisher.departmentDepartment of Mathematicsen


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