Regularized Small Area Estimation with Graph Laplacian Benchmarking priors
The framework introduces a Bayesian method for Small Area Estimation that combines borrowing information across areas with benchmarking estimates against reliable aggregates. It develops new Benchmarking priors, including graph-based regularizations using Laplacian matrices derived from area similarities, to improve estimation precision.