A0564
Title: Entropy based small area estimation for count data with spatial effects
Authors: Rossella Bernardini Papalia - University of Bologna (Italy) [presenting]
Esteban Fernandez Vazquez - University of Oviedo (Spain)
Abstract: Statistical information for empirical analysis is very frequently available at a higher level of aggregation than it would be desired. Small area estimation is important in light of a continual demand by data users for finer geographic detail of official statistics and for various subgroups. The spatial disaggregation of the socio-economic data is considered complex owing to the inherent spatial properties and relationships of the spatial data, namely, spatial dependence and spatial heterogeneity. The spatial dependence, spatial heterogeneity and the effect of scale produce major technical issues that largely impact on the accuracy of the small area estimates. We propose entropy-based small area estimation methods for count areal data that introduce spatial effects by using all available information at each level of aggregation even if it is incomplete. The proposed methods are validated through the Monte Carlo simulations using ancillary information. An empirical application to real data is also presented.
