When data are aggregated from a finer to a coarser geographical level, there will be loss of information known as the scaling problem in geography. To address the scaling problem, we propose to use a joint convolution model that describes the risk variation at both the finer and coarser levels simultaneously by sharing both the correlated and the uncorrelated components. We compare our model with the naive approach that ignores the scale effect in real and simulated data in a range of criteria such as deviance information criterion (DIC), Watanabe-Akaike information criterion, and mean square prediction error (MSPE). We found that our multiscale model is better than the naive model.
['Mehreteab Aregay', 'Andrew B. Lawson', 'Christel Faes', 'Russell S. Kirby', 'Rachel Carroll', 'Kevin Watjou']
Multiscale modeling approach for hierarchical aligned aggregated small area health data