Sampling distributions for complex design variance estimators in a Fay-Herriot model

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 distributions, and the resulting FH models with variance smoothing to the standard FH model. We find that the standard model exhibits undercoverage, while the variance smoothing models produce better credible intervals according to proper scoring rules. Interestingly, the simple sampling distribution, which is easiest to implement, performs equally as well as the more complex sampling distribution. We illustrate the proposed models by estimating height-for-age z-scores using the 2022 Kenya DHS.