← Back to all news
arXiv Statistics ML By Jairo F\'uquene-Pati\~no, Brenda Betancourt

Regularized Small Area Estimation with Graph Laplacian Benchmarking priors

Read the original on arXiv Statistics ML →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

Related stories

Hugging Face Trending Papers
Oct 8

Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations

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.

rag
More like this →
arXiv Machine Learning
Oct 9

Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations

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.

By Chaitanya Jobanputra, Sebastian Vollmer, Gerrit Gro{\ss}mann
rag
More like this →
arXiv Statistics ML
Oct 2

Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States

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.

By Hammed A. Olayinka, Saheed O. Olayemi
diffusion
More like this →