New research proposes horseshoe prior for robust spatial small area estimation

A new paper introduces the horseshoe prior as a robust and effective method for small area estimation, particularly in spatial contexts. The research details a tail-robustness theorem showing the horseshoe model can bound the influence of outlying direct estimates, unlike Gaussian models. It also provides theoretical and empirical evidence on when global-local shrinkage or structured smoothing is more advantageous, concluding that the horseshoe prior is a sound default choice for area effects due to its aggressive borrowing of strength while allowing exceptional areas to be identified. AI

RANK_REASON The cluster contains an academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

• Bhadra et al.
• Carvalho et al.
• Ghosh and Rao
• Horseshoe Priors
• Small Area Estimation

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