A resampling approach to estimation of the linking variance in the Fay–Herriot model
Snigdhansu Chatterjee
Statistical Theory and Related Fields, 2019, vol. 3, issue 2, 170-177
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.
Date: 2019
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2019.1675408 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tstfxx:v:3:y:2019:i:2:p:170-177
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20
DOI: 10.1080/24754269.2019.1675408
Access Statistics for this article
Statistical Theory and Related Fields is currently edited by Zhao Wei
More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().