id,collection,dc.contributor.author,dc.date.accessioned,dc.date.available,dc.date.issued,dc.description.abstract[en],dc.format.extent,dc.identifier.uri,dc.language,dc.rights.uri,dc.subject.ddc,dc.subject[en],dc.title,dc.type,dcterms.accessRights.openaire,dcterms.bibliographicCitation.doi,dcterms.bibliographicCitation.journaltitle,dcterms.bibliographicCitation.number,dcterms.bibliographicCitation.pageend,dcterms.bibliographicCitation.pagestart,dcterms.bibliographicCitation.url,dcterms.bibliographicCitation.volume,dcterms.isPartOf.eissn,refubium.affiliation,refubium.affiliation.other,refubium.resourceType.isindependentpub,refubium.resourceType.provider "cb5de079-1a50-4749-a53a-09bef11f2b57","fub188/16","Krennmair, Patrick||Schmid, Timo","2023-01-05T12:42:23Z","2023-01-05T12:42:23Z","2022","This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes. Small area estimators are predominantly conceptualised within the regression-setting and rely on linear mixed models to account for the hierarchical structure of the survey data. In contrast, machine learning methods offer non-linear and non-parametric alternatives, combining excellent predictive performance and a reduced risk of model-misspecification. Mixed effects random forests combine advantages of regression forests with the ability to model hierarchical dependencies. This paper provides a coherent framework based on mixed effects random forests for estimating small area averages and proposes a non-parametric bootstrap estimator for assessing the uncertainty of the estimates. We illustrate advantages of our proposed methodology using Mexican income-data from the state Nuevo León. Finally, the methodology is evaluated in model-based and design-based simulations comparing the proposed methodology to traditional regression-based approaches for estimating small area averages.","30 Seiten","https://refubium.fu-berlin.de/handle/fub188/37457||http://dx.doi.org/10.17169/refubium-37170","eng","https://creativecommons.org/licenses/by/4.0/","500 Naturwissenschaften und Mathematik::510 Mathematik::510 Mathematik","mean squared error||official statistics||small area estimation||tree-based methods","Flexible domain prediction using mixed effects random forests","Wissenschaftlicher Artikel","open access","10.1111/rssc.12600","Journal of the Royal Statistical Society: Series C (Applied Statistics)","5","1894","1865","https://doi.org/10.1111/rssc.12600","71","1467-9876","Wirtschaftswissenschaft","Volkswirtschaftslehre / Institut für Statistik und Ökonometrie:::9a3266c5-7bc3-4e9e-873b-411202642a32:::600","no","WoS-Alert"