Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19998
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: The R package emdi for estimating and mapping regionally disaggregated indicators
Authors: Kreutzmann, Ann-Kristin
Pannier, Sören
Rojas-Perilla, Natalia
Schmid, Timo
Templ, Matthias
Tzavidis, Nikos
et. al: No
DOI: 10.18637/jss.v091.i07
10.21256/zhaw-19998
Published in: Journal of Statistical Software
Volume(Issue): 91
Issue: 7
Issue Date: 2019
Publisher / Ed. Institution: Foundation for Open Access Statistics
ISSN: 1548-7660
Language: English
Subjects: Official statistics; Survey statistics; Parallel computing; Small area estimation; Visualization
Subject (DDC): 005: Computer programming, programs and data
500: Natural sciences and mathematics
Abstract: The 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/19998
Fulltext version: Published version
License (according to publishing contract): CC BY-NC 4.0: Attribution - Non commercial 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in Collections:Publikationen School of Engineering

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