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dc.contributor.authorKing, Gary
dc.contributor.authorRosen, Ori
dc.contributor.authorTanner, Martin
dc.date.accessioned2010-05-22T15:35:52Z
dc.date.issued1999
dc.identifier.citationKing, Gary, Ori Rosen, and Martin Tanner. 1999. Binomial-beta hierarchical models for ecological inference. Sociological Methods and Research 28(1): 61-90.en_US
dc.identifier.issn0049-1241en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4125130
dc.description.abstracthe authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King’s ecological inference model. The new approach reveals some features of the data that King’s approach does not, can be easily generalized to more complicated problems such as general R C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the significance of the covariates. It may also be better suited to cases in which the observed aggregate cells are estimated from very few observations or have some forms of measurement error. This article also provides an example of a hierarchical model in which the statistical idea of “borrowing strength” is used not merely to increase the efficiency of the estimates but to enable the data analyst to obtain estimates.en_US
dc.description.sponsorshipStatisticsen_US
dc.language.isoen_USen_US
dc.publisherSAGE Publicationsen_US
dc.relation.isversionofdoi:10.1177/0049124199028001004en_US
dc.relation.hasversionhttp://gking.harvard.edu/files/binom.pdfen_US
dash.licenseLAA
dc.titleBinomial-Beta Hierarchical Models for Ecological Inferenceen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalSociological Methods and Researchen_US
dash.depositing.authorKing, Gary
dc.date.available2010-05-22T15:35:52Z
dc.identifier.doi10.1177/0049124199028001004*
dash.identifier.orcid0000-0002-5327-7631*
dash.contributor.affiliatedKing, Gary
dc.identifier.orcid0000-0002-5327-7631


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