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dc.contributor.authorScott, Andrew John
dc.date.accessioned2014-10-30T04:30:17Z
dc.date.available2014-10-30T04:30:17Z
dc.date.issued2014-05
dc.identifier.otherscott_andrew_j_201405_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/scott_andrew_j_201405_ms
dc.identifier.urihttp://hdl.handle.net/10724/30624
dc.description.abstractSmall area estimation is a topic of considerable interest for many agencies wishing to use data collected at the national level to accurately estimate statistics speci c to subsets of the general population. Our approach used a lognormal multivariate hierarchical Bayes (MVHB) mixed model to generate improved estimates of small area totals for Census of Governments data drawn through strati ed random sampling (SRS) and skewed data drawn through probability proportional to size (PPS) sampling. Additionally, we explored the inclusion of design weights as model covariates for PPS-sampled data. Gibbs samplers were developed to provide estimates of posterior mean and variance. We show that the MVHB models produced improved estimates with decreased posterior variance, when compared to their univariate counterparts for both the PPS and SRS samples.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectMultivariate hierarchical Bayes
dc.subjectlognormal models
dc.subjectGibbs sampling
dc.subjectmultivariate mixed linear model
dc.subjectsmall area estimation
dc.subjectdesign weights
dc.subjectprobability proportional to size
dc.titleEstimation of government employment using multivariate hierarchical Bayes modeling
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorGauri Datta
dc.description.committeeGauri Datta
dc.description.committeeAbhyuday Mandal
dc.description.committeeKimberly Love-Myers


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