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dc.contributor.authorBroniecki, Philipp
dc.contributor.authorLeemann, Lucas
dc.contributor.authorWuest, Reto
dc.date.accessioned2021-11-29T11:17:41Z
dc.date.available2021-11-29T11:17:41Z
dc.date.created2021-01-31T11:34:23Z
dc.date.issued2022
dc.identifier.issn0022-3816
dc.identifier.urihttps://hdl.handle.net/11250/2831883
dc.descriptionUnder embargo until: 2022-10-19
dc.description.abstractMultilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include context-level information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of context-level variables has been informed by a rich tradition of public opinion research.en_US
dc.language.isoengen_US
dc.publisherThe University of Chicago Pressen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleImproved Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)en_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 Southern Political Science Associationen_US
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doihttps://doi.org/10.1086/714777
dc.identifier.cristin1883632
dc.source.journalJournal of Politicsen_US
dc.relation.projectEC/H2020/741538en_US
dc.relation.projectEC/H2020/804288en_US
dc.identifier.citationJournal of Politics. 2022, 84 (1)en_US
dc.source.volume84en_US
dc.source.issue1en_US


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell 4.0 Internasjonal