Horseshoe Priors for Spatial Small Area Estimation: Regular Variation, Tail Robustness, and Deep Learning

arXiv:2606.30659v1 Announce Type: cross Abstract: Small area estimation borrows strength across domains to repair the poor precision of direct survey estimators. Two philosophies dominate the area-level literature. The first, descending from Ghosh and Rao (1994), borrows strength through structured Gaussian smoothing: an intrinsic conditional autoregression or its BYM2 reparameterization pools each area towards its neighbours. The second borrows strength globally but acts locally through a heavy-tailed global-local prior on area effects, of which the horseshoe of Carvalho et al. (2010) is the