Smoothed Model-Assisted Small Area Estimation

TL;DR

This paper introduces a new smoothed model-assisted estimator for small area estimation that effectively combines survey design considerations with covariate and spatial information, improving accuracy in data-scarce settings.

Contribution

It proposes a novel estimator that is both design- and model-consistent, integrating survey design, covariates, and spatial smoothing in small area estimation.

Findings

The estimator is shown to be both design- and model-consistent under certain assumptions.

It outperforms existing methods in simulations and real data applications.

The approach effectively leverages covariate and spatial information to improve estimate accuracy.

Abstract

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.