A Unifying Parametric Framework for Estimating Finite Population Totals from Complex Samples

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dc.contributor.advisorBrick, J. Michaelen_US
dc.contributor.advisorKreuter, Fraukeen_US
dc.contributor.authorFlores Cervantes, Ismaelen_US
dc.contributor.departmentSurvey Methodologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2019-06-21T05:35:38Z
dc.date.available2019-06-21T05:35:38Z
dc.date.issued2019en_US
dc.description.abstractWe propose a unifying framework for improving the efficiency of design-based estimators of finite population characteristics in the presence of full response. We call the framework a Parametric (PA) approach. The PA framework, an extension of the model-assisted theory, uses an algorithmic approach driven by the observed data. The algorithm identifies the relevant subset of auxiliary variables related to the outcome, and the known population totals of these variables are used to compute the PA estimator. We apply the PA framework to three important estimation problems: the identification of the functional form of a design-based estimator based on the observed data; the identification working or assisting model; and the development of the methodology for creating new design-based estimators. The PA estimators are theoretically justified and evaluated by simulations. This dissertation is limited to single-stage sample designs with full response, but the framework can be extended to other sample designs and for estimation with nonresponse.en_US
dc.identifierhttps://doi.org/10.13016/0xvh-jcex
dc.identifier.urihttp://hdl.handle.net/1903/22113
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledDesign-based estimationen_US
dc.subject.pquncontrolledEfficient estimation of totalsen_US
dc.subject.pquncontrolledModel-assisted estimationen_US
dc.subject.pquncontrolledModel buildingen_US
dc.subject.pquncontrolledVariable selectionen_US
dc.titleA Unifying Parametric Framework for Estimating Finite Population Totals from Complex Samplesen_US
dc.typeDissertationen_US

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