Exact adaptive confidence intervals for small areas

TL;DR

This paper introduces an exact adaptive confidence interval method for small area estimation that guarantees constant coverage regardless of the true area mean, leveraging auxiliary information and avoiding model-dependent coverage issues.

Contribution

It proposes a novel confidence interval procedure that ensures constant coverage for small areas, independent of the heterogeneity model, and shortens expected length by using auxiliary data.

Findings

Guarantees constant $1-eta$ coverage for all area means.

Uses auxiliary information to improve interval efficiency.

Coverage does not depend on the heterogeneity model.

Abstract

In the analysis of survey data it is of interest to estimate and quantify uncertainty about means or totals for each of several non-overlapping subpopulations, or areas. When the sample size for a given area is small, standard confidence intervals based on data only from that area can be unacceptably wide. In order to reduce interval width, practitioners often utilize multilevel models in order to borrow information across areas, resulting in intervals centered around shrinkage estimators. However, such intervals only have the nominal coverage rate on average across areas under the assumed model for across-area heterogeneity. The coverage rate for a given area depends on the actual value of the area mean, and can be nearly zero for areas with means that are far from the across-group average. As such, the use of uncertainty intervals centered around shrinkage estimators are inappropriate when area-specific coverage rates are desired. In this article, we propose an alternative confidence interval procedure for area means and totals under normally distributed sampling errors. This procedure not only has constant $1-\alpha$ frequentist coverage for all values of the target quantity, but also uses auxiliary information to borrow information across areas. Because of this, the corresponding intervals have shorter expected lengths than standard confidence intervals centered on the unbiased direct estimator. Importantly, the coverage of the procedure does not depend on the assumed model for across-area heterogeneity. Rather, improvements to the model for across-area heterogeneity result in reduced expected interval width.