model we exploit the link between the Asymmetric Laplace Distribution and maximum likelihood estimation for quantile …-variance relationship. Mean Squared Error estimation is discussed. Extensive model-based simulations are used for comparing the proposed …

Modern systems of official statistics require the timely estimation of area-specific densities of sub …-parametric kernel density estimation that reverses the rounding process by using a Bayesian measurement error model. The methodology is …

Among a variety of small area estimation methods, one popular approach for the estimation of linear and non …-linear indicators is the empirical best predictor. However, parameter estimation using standard maximum likelihood methods is not … is often the case for income variables. Therefore, this work proposes an estimation method, which enables the estimation …

kernel density estimates (KDE) offer advantages over classical choropleth maps. However, kernel density estimation needs … aggregates for kernel density estimation. Their algorithm simulates "exact" geo-coordinates which reflect the information on the …, the original algorithm is modified to cope with the estimation of a ratio of two densities. Our empirical examples refer …