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The section provides an overview of the basic unitlevel model and some of the extensions.

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How to applyBesides a basic understanding of the methodology, the application of approaches with statistical software is of interest for practitioners. While the inputs partly depend on the chosen software packages, some common data inputs and inputs for some implemented extensions are described. Inputs The starting point of the application is unitlevel data, i.e. a survey is available at unitlevel (microdata) containing the domain identifying variables. For the auxiliary information, the requirements differ depending on the indicator of interest and thus the chosen method:
Additional to the variable of interest and the predictor variables, the variable indicating domains or clusters needs to be given. For the other necessary arguments as e.g., the estimation method of the model parameters, defaults (predefined values) are often set to simplify the application. Outputs The outputs of all software packages are domainspecific estimates, Furthermore, all software packages provide estimates of the mean squared error. Implementation The table below gives a first overview of modules in standard software that provide the application of the basic unitlevel model, the ELL and the EBP. Other extensions to the basic unitlevel model are described below. Overview of availability of the unitlevel model in statistical software (not comprehensive)

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Uncertainty measurement
All software packages report estimates for the uncertainty along with the SAE estimators. For the basic unitlevel model, estimators of the mean squared error (MSE) have either analytical formulations, are derived by replication methods or obtained by the posterior variance when HB estimation is used. For the ELL and EBP, replication methods in form of bootstraps are available. 
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Extensions
Additional to the basic unitlevel model, some extensions are implemented in standard software. In the following, the ideas behind the extensions are briefly described and the table gives an overview which package provides the extension. For detailed information about the methodology, please see the original reference or the package descriptions/vignettes.

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ReferencesHuang, R. and M. Hidiroglou (2003). Design consistent estimators for a mixed linear model on survey data. Proceedings of the Survey Research Methods Section, American Statistical Association (2003), 1897–1904. Rao, J.N.K. and Molina, I. (2015). Small Area Estimation. New York: Wiley. 