In the Fay–Herriot model, we consider estimators of the linking variance obtained using different types of resampling schemes. The usefulness of this approach is that even when the estimator from the original data falls below zero or any other specified threshold, several of the resamples can potentially yield values above the threshold. We establish asymptotic consistency of the resampling-based estimator of the linking variance for a wide variety of resampling schemes and show the efficacy of using the proposed approach in numeric examples.
Bibliographical noteFunding Information:
This research is partially supported by the National Science Foundation (NSF) [grant numbers # DMS-1622483 and # DMS-1737918]. The author thanks the reviewers and editors for their comments, which helped improve the paper.
- Bayesian bootstrap
- Linking variance
- Prasad–Rao estimator
- m-out-of-n bootstrap
- paired bootstrap