The <i>R</i> Package <b>emdi</b> for Estimating and Mapping Regionally Disaggregated Indicators

Kreutzmann, Ann-Kristin; Pannier, Soeren; Rojas‐Perilla, Natalia; Schmid, Timo; Templ, Matthias; Tzavidis, Nikos

doi:10.18637/jss.v091.i07

Cited by 46 publications

(40 citation statements)

References 24 publications

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“…Code in R for computing EBP estimates that we discussed in Section 3.2 that includes an option for using the transformations discussed in the present paper, visualization and export of the results to Excel is proposed in the package emdi by Kreutzmann et al . (). Collections of R functions for implementing a wide range of SAE methods are available in the documentations of national‐ and European‐funded research projects.…”

Section: An Update On Small Area Estimation Softwarementioning

confidence: 97%

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Tzavidis

Zhang

Luna

et al. 2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Small area estimation is a research area in official and survey statistics of great practical relevance for national statistical institutes and related organizations. Despite rapid developments in methodology and software, researchers and users would benefit from having practical guidelines for the process of small area estimation. We propose a general framework for the production of small area statistics that is governed by the principle of parsimony and is based on three broadly defined stages, namely specification, analysis and adaptation, and evaluation. Emphasis is given to the interaction between a user of small area statistics and the statistician in specifying the target geography and parameters in the light of the available data. Model‐free and model‐dependent methods are described with a focus on model selection and testing, model diagnostics and adaptations such as use of data transformations. Uncertainty measures and the use of model and design‐based simulations for method evaluation are also at the centre of the paper. We illustrate the application of the proposed framework by using real data for the estimation of non‐linear deprivation indicators. Linear statistics, e.g. averages, are included as special cases of the general framework.

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Section: An Update On Small Area Estimation Softwarementioning

confidence: 97%

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Tzavidis

Zhang

Luna

et al. 2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…The SEM algorithm uses 40 burn-in iterations and 200 additional iterations. Note that the estimators above are available in the packages emdi (Kreutzmann et al, 2019) and smicd (Walter, 2019b) in R. The performance of point estimates is assessed by computing the area-specific empirical root mean squared error RMSE…”

Section: Model -Based Simulationsmentioning

confidence: 99%

Domain Prediction with Grouped Income Data

Walter

Groß

Schmid

et al. 2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…the approach of Elbers et al (2003). The methods that are proposed in this paper can be implemented by using the R package emdi (Kreutzmann et al, 2019). The package supports the user by estimating and mapping regionally disaggregated indicators.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Data-Driven Transformations in Small Area Estimation

Rojas‐Perilla

Pannier

Schmid

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Small area models typically depend on the validity of model assumptions. For example, a commonly used version of the empirical best predictor relies on the Gaussian assumptions of the error terms of the linear mixed regression model: a feature rarely observed in applications with real data. The paper tackles the potential lack of validity of the model assumptions by using data‐driven scaled transformations as opposed to ad hoc chosen transformations. Different types of transformations are explored, the estimation of the transformation parameters is studied in detail under the linear mixed regression model and transformations are used in small area prediction of linear and non‐linear parameters. The use of scaled transformations is crucial as it enables fitting the linear mixed regression model with standard software and hence it simplifies the work of the data analyst. Mean‐squared error estimation that accounts for the uncertainty due to the estimation of the transformation parameters is explored by using the parametric and semiparametric (wild) bootstrap. The methods proposed are illustrated by using real survey and census data for estimating income deprivation parameters for municipalities in the Mexican state of Guerrero. Simulation studies and the results from the application show that using carefully selected, data‐driven transformations can improve small area estimation.

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The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

Cited by 46 publications

References 24 publications

From Start to Finish: A Framework for the Production of Small Area Official Statistics

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Domain Prediction with Grouped Income Data

Data-Driven Transformations in Small Area Estimation

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