A resampling approach to estimation of the linking variance in the Fay–Herriot model

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)170-177
Number of pages8
JournalStatistical Theory and Related Fields
Volume3
Issue number2
DOIs
StatePublished - Jul 3 2019

Bibliographical note

Funding 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.

Publisher Copyright:
© 2019, © East China Normal University 2019.

Keywords

  • Bayesian bootstrap
  • Linking variance
  • Prasad–Rao estimator
  • m-out-of-n bootstrap
  • paired bootstrap

Fingerprint Dive into the research topics of 'A resampling approach to estimation of the linking variance in the Fay–Herriot model'. Together they form a unique fingerprint.

Cite this