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

Snigdhansu Chatterjee

doi:10.1080/24754269.2019.1675408

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

Snigdhansu Chatterjee

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	170-177
Number of pages	8
Journal	Statistical Theory and Related Fields
Volume	3
Issue number	2
DOIs	https://doi.org/10.1080/24754269.2019.1675408
State	Published - 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

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

Abstract

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Keywords

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