Small-area estimation techniques have typically relied on plug-in estimation based on models containing random area effects. More recently, regression M-quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the...

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is...

In this paper we perform a statistical evaluation of whether it is worthwhile, in economic terms, to resist to extortion demands by the maa, from the point of view of rms operating in an area dominated by criminal organizations. We use a unique collected and matched database on rm...

This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical Best Linear...

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...

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...