# Smoothed Model-Assisted Small Area Estimation

arXiv: https://arxiv.org/abs/2201.08775 · HTML: https://mlsanity.com/paper/2201.08775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2022.

## Built on by (most cited first, 1 of 1)

- [Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation](https://mlsanity.com/paper/2609.20758/) (2026)

## Abstract (arXiv)

In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore survey design, while traditional small area estimation approaches may not incorporate both unit level covariate information and spatial smoothing in a design-consistent way. We propose a smoothed model-assisted estimator that accounts for survey design and leverages both unit level covariates and spatial smoothing. Under certain assumptions, this estimator is both design-consistent and model-consistent. We compare it with existing design-based and model-based estimators using real and simulated data.