B1906
Title: Global-local priors for spatial small area estimation
Authors: Xueying Tang - University of Arizona (United States) [presenting]
Malay Ghosh - University of Florida (United States)
Abstract: Small area estimation is gaining increasing popularity among survey statisticians. Since the direct estimates of small areas usually have large standard errors, model-based approaches are often adopted to borrow strength across areas. The models often use covariates to link different areas and random effects to account for the additional variation. In the classic Fay-Herriot model, the random effects are assumed to have independent normal distributions with a shared variance. Recent studies showed that random effects are not necessary for all areas, so global-local priors have been introduced in the literature to effectively characterize the sparsity in random effects. Global-local priors are introduced in the context of small-area estimation where the area-level random effects exhibit a spatial structure. The findings are illustrated via both simulation and real data examples.
