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

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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

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Keywords

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

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