The paper introduces a Bayesian framework for small area estimation that jointly handles borrowing information across areas and benchmarking estimates to reliable aggregates via a new family of Benchmarking priors. These priors arise from a benchmark-constrained regularization problem and include a Benchmarking Prior without additional regularization, as well as Single and Multi-View Laplacian Benchmarking Priors that use graph Laplacians built from area similarities based on external covariates. Tailored MCMC algorithms are developed for posterior computation, and the models are evaluated through a data-based simulation and applied to estimate Average Household Size at the municipality level in Colombia in 2025, where graph-based regularization improves performance and Multi-View models yield more precise estimates.
arXiv:2608.23802v1 Announce Type: cross Abstract: Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), images appear as pixels (lattice graph),...
arXiv:2410.11771v4 Announce Type: replace Abstract: Many spatial models exhibit locality structures that effectively reduce their intrinsic dimensionality, enabling efficient approximation and sampli...
arXiv:2604. 07635v2 Announce Type: replace-cross Abstract: This research considers a scalable inference for spatial data modeled through Gaussian intrinsic conditional autoregressive (ICAR) structures.
Puffin is a probabilistic framework that disaggregates coarse spatial totals into high‑resolution estimates by leveraging satellite embeddings as covariates. Unlike deterministic methods, it learns a probability distribution for each fine‑scale subregion and trains with an aggregation‑aware likelihood, ensuring that predictions sum to the observed regional total. The approach provides calibrated uncertainty and does not require fine‑resolution labels, and it has been evaluated on German and US census, employment, and election data.
Puffin is a probabilistic framework that disaggregates coarse spatial totals into high-resolution estimates by leveraging satellite embeddings as covariates. Unlike deterministic methods, it learns a probability distribution for each fine-scale subregion and trains using an aggregation-aware likelihood, ensuring that predictions sum to the observed regional totals. The approach provides calibrated uncertainty and does not require fine-resolution labels, and it has been evaluated on German and US census, employment, and election data.
The paper introduces DSP‑BART‑HS, a Dynamic Spatial Panel Bayesian Additive Regression Trees model with Horseshoe shrinkage, designed for high‑dimensional spatio‑temporal panel data. Across nine simulated scenarios, the model outperforms or matches a wide range of spatial econometric, non‑parametric machine learning, and small‑area estimators, especially when individual‑level non‑linearity drives outcome variance. The authors validate the method on two U.S. county‑level applications—intergenerational economic mobility and geographic income inequality—showing strong predictive accuracy even under unseen‑region, random, and temporal holdouts, while noting a temporal extrapolation advantage for a simpler autoregressive model.
arXiv:2609.15305v1 Announce Type: cross Abstract: Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use informati...
arXiv:2609.23668v1 Announce Type: cross Abstract: Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constra...
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
arXiv:2609.39712v1 Announce Type: cross Abstract: We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation. Bui...
arXiv:2609.36046v1 Announce Type: cross Abstract: In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across...
arXiv:2608. 07871v1 Announce Type: cross Abstract: Accurate, up-to-date income data at the sub-municipal scale is essential for social policy in middle-income countries, yet in Brazil it depends on a costly decennial census whose intercensal gap recently exceeded a decade.