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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19998
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dc.contributor.authorKreutzmann, Ann-Kristin-
dc.contributor.authorPannier, Sören-
dc.contributor.authorRojas-Perilla, Natalia-
dc.contributor.authorSchmid, Timo-
dc.contributor.authorTempl, Matthias-
dc.contributor.authorTzavidis, Nikos-
dc.date.accessioned2020-05-07T08:49:10Z-
dc.date.available2020-05-07T08:49:10Z-
dc.date.issued2019-
dc.identifier.issn1548-7660de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19998-
dc.description.abstractThe R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria.de_CH
dc.language.isoende_CH
dc.publisherFoundation for Open Access Statisticsde_CH
dc.relation.ispartofJournal of Statistical Softwarede_CH
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/de_CH
dc.subjectOfficial statisticsde_CH
dc.subjectSurvey statisticsde_CH
dc.subjectParallel computingde_CH
dc.subjectSmall area estimationde_CH
dc.subjectVisualizationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleThe R package emdi for estimating and mapping regionally disaggregated indicatorsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.18637/jss.v091.i07de_CH
dc.identifier.doi10.21256/zhaw-19998-
zhaw.funding.euNode_CH
zhaw.issue7de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume91de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Kreutzmann, A.-K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for estimating and mapping regionally disaggregated indicators. Journal of Statistical Software, 91(7). https://doi.org/10.18637/jss.v091.i07
Kreutzmann, A.-K. et al. (2019) ‘The R package emdi for estimating and mapping regionally disaggregated indicators’, Journal of Statistical Software, 91(7). Available at: https://doi.org/10.18637/jss.v091.i07.
A.-K. Kreutzmann, S. Pannier, N. Rojas-Perilla, T. Schmid, M. Templ, and N. Tzavidis, “The R package emdi for estimating and mapping regionally disaggregated indicators,” Journal of Statistical Software, vol. 91, no. 7, 2019, doi: 10.18637/jss.v091.i07.
KREUTZMANN, Ann-Kristin, Sören PANNIER, Natalia ROJAS-PERILLA, Timo SCHMID, Matthias TEMPL und Nikos TZAVIDIS, 2019. The R package emdi for estimating and mapping regionally disaggregated indicators. Journal of Statistical Software. 2019. Bd. 91, Nr. 7. DOI 10.18637/jss.v091.i07
Kreutzmann, Ann-Kristin, Sören Pannier, Natalia Rojas-Perilla, Timo Schmid, Matthias Templ, and Nikos Tzavidis. 2019. “The R Package Emdi for Estimating and Mapping Regionally Disaggregated Indicators.” Journal of Statistical Software 91 (7). https://doi.org/10.18637/jss.v091.i07.
Kreutzmann, Ann-Kristin, et al. “The R Package Emdi for Estimating and Mapping Regionally Disaggregated Indicators.” Journal of Statistical Software, vol. 91, no. 7, 2019, https://doi.org/10.18637/jss.v091.i07.


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