In general, researchers have been using data transformations as a go-to tool to assist scientific work under the classical and linear mixed regression models instead of developing new theories, applying complex methods or extending software functions. However, transformations are often...

Im Allgemeinen nutzen Forscherinnen und Forscher Daten-Transformationen als Hilfsmittel für die wissenschaftliche Arbeit unter den klassischen und linearen gemischten Regressionsmodellen, anstatt neue Theorien zu entwickeln, komplexe Methoden anzuwenden oder Softwarefunktionen zu erweitern....

After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, this paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the World...

This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside...

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 model, a feature rarely observed in applications with real data. The present...

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 model, a feature rarely observed in applications with real data. The present...