Regularized Small Area Estimation with Graph Laplacian Benchmarking priors
The resulting family includes a Benchmarking Prior that incorporates the benchmarking restrictions without additional regularization across areas, and Single and Multi-View Laplacian Benchmarking Priors that introduce regularization through graph Laplacians constructed from area similarities based on external covariate information.
Key points
- Small area estimation (SAE) often requires both borrowing information across areas and benchmarking estimates to reliable aggregates.
- We develop a Bayesian framework that addresses these two objectives jointly through a new family of Benchmarking priors.
- For posterior computation under these degenerate priors, we develop tailored MCMC algorithms based on a reduced parameterization of the constraint space.
- We assess the proposed models using a data-based simulation and apply the framework to estimate Average Household Size (AHS) at the municipality level in Colombia in 2025.
Sources (1)
- [1]Regularized Small Area Estimation with Graph Laplacian Benchmarking priorsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:23 AM
The resulting family includes a Benchmarking Prior that incorporates the benchmarking restrictions without additional regularization across areas, and Single and Multi-View Laplacian Benchmarking Priors that introduce regularization through graph Laplacians constructed from area similarities based on external covariate information.
Small area estimation (SAE) often requires both borrowing information across areas and benchmarking estimates to reliable aggregates.
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