B0933
Title: Single source small area estimation
Authors: Jan Pablo Burgard - Trier University (Germany) [presenting]
Domingo Morales - University Miguel Hernandez of Elche (Spain)
Joscha Krause - Trier University (Germany)
Abstract: Regional indicators are important for business decisions and policymaking. Most regional information is not collected in registers, but gathered in surveys. Due to disclosure reasons and small sample sizes, only aggregated, imprecise totals or mean estimates are provided. Small area models, that aim to improve the precision of the regional estimates, must explicitly account for data uncertainty to allow for reliable results. This can be achieved via measurement error models that introduce distribution assumptions on the noisy data. However, these methods usually require target and explanatory variable errors to be independent. This does not hold when data for both have been estimated from the same survey, which is sometimes the case in official statistics or special purpose surveys. If not accounted for, prevalence estimates can be severely biased. We propose a new measurement error model for regional prevalence estimation that is suitable for settings where target and explanatory variable errors are dependent. We derive the empirical best predictors and demonstrate mean-squared error estimation. A maximum likelihood approach for model parameter estimation is presented. Simulation experiments are conducted to prove the effectiveness of the method. An application to regional hypertension prevalence estimation in Germany is provided.
