Modern systems of official statistics require the timely estimation of area-specific densities of sub-populations. Ideally estimates should be based on precise geo-coded information, which is not available due to confidentiality constraints. One approach for ensuring confidentiality is by...

The transformation of area aggregates between non-hierarchical area systems is a standard problem of official statistics. We introduce a new method which is based on kernel density estimates. It is a modification of the SEM algorithm proposed by Gross et al. (2016), which was used for the...

One popular small area estimation method for estimating poverty and inequality indicators is the empirical best predictor under the unit‐level nested error regression model with a continuous dependent variable. However, parameter estimation is more challenging when the response variable is...

Composite spatial data on administrative area level are often presented by maps. The aim is to detect regional differences in the concentration of subpopulations, like elderly persons, ethnic minorities, low-educated persons, voters of a political party or persons with a certain disease....

Map-based regional analysis is interested to detect areas with a large concentration of certain populations. Here kernel density estimates (KDE) offer advantages over classical choropleth maps. However, kernel density estimation needs exact geo-coordinates. In a recent paper Groß et al. (2017)...

In self-reported data usually a phenomenon called 'heaping' occurs, i.e. survey participants round the values of their income, weight or height to some degree. Additionally, respondents may be more prone to round off or up due to social desirability. By ignoring the heaping process a severe bias...

Spatially disaggregated income indicators are typically estimated by using model‐based methods that assume access to auxiliary information from population micro‐data. In many countries like Germany and the UK population micro‐data are not publicly available. In this work we propose small...

Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small area estimation. Recently proposed outlier robust...

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of...

The R package emdi offers a methodological and computational framework for the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for assessing, processing and presenting the results. A range of indicators that includes the mean of the target...