Sampling distributions for complex design variance estimators in a Fay-Herriot model
Alana McGovern, Geir-Arne Fuglstad, and Jon Wakefield

TL;DR
This paper develops and compares new sampling distributions for variance estimators in Fay-Herriot models tailored to complex survey designs like DHS, improving uncertainty quantification in small sample domains.
Contribution
It derives two new sampling distributions for DHS survey designs and demonstrates their effectiveness in variance smoothing models through simulations and real data application.
Findings
Standard FH models show undercoverage in complex designs.
Variance smoothing models yield better credible intervals.
Simple sampling distribution performs as well as the complex one.
Abstract
Fay-Herriot (FH) models with variance smoothing typically use chi-squared sampling distributions for the design variance estimators. This choice is only valid under strong assumptions on the population and the sampling design, and the choice of sampling distribution is understudied for complex survey designs such as the stratified two-stage clustering design used by the Demographic and Health Surveys (DHS). DHS conducts surveys in low- and middle-income countries and result in low sample sizes for unplanned domains of interest. Thus, accounting for the uncertainty in the estimated design variances is important. We derive two sampling distributions under the DHS design, a simple and a more complex, while clearly specifying and discussing the required superpopulation and design assumptions. In a simulation study, we compare the two sampling distributions to the empirical sampling…
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