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dc.date.accessioned2021-04-30T18:48:57Z
dc.date.created2021-01-31T11:34:23Z
dc.date.issued2021
dc.identifier.citationBroniecki, Philipp Leemann, Lucas Wuest, Reto . Improved Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP). Journal of Politics. 2021
dc.identifier.urihttp://hdl.handle.net/10852/85772
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.
dc.languageEN
dc.titleImproved Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)
dc.typeJournal article
dc.creator.authorBroniecki, Philipp
dc.creator.authorLeemann, Lucas
dc.creator.authorWuest, Reto
dc.date.embargoenddate2022-04-30
cristin.unitcode185,0,0,0
cristin.unitnameUniversitetet i Oslo
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.cristin1883632
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Politics&rft.volume=&rft.spage=&rft.date=2020
dc.identifier.jtitleJournal of Politics
dc.identifier.doihttps://doi.org/10.1086/714777
dc.identifier.urnURN:NBN:no-88444
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0022-3816
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/85772/2/broniecki-etal-jop-2020.pdf
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/85772/3/broniecki-etal-jop-2020-app.pdf
dc.type.versionAcceptedVersion
dc.relation.projectEC/H2020/741538
dc.relation.projectEC/H2020/804288


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