Application of Sampling Variance Smoothing Methods for Small Area Proportion Estimation

You Yong () and Hidiroglou Mike ()
Additional contact information
You Yong: Statistics Canada, Ottawa, K1A 0T6, Canada
Hidiroglou Mike: Statistics Canada, Ottawa, K1A 0T6, Canada

Journal of Official Statistics, 2023, vol. 39, issue 4, 571-590

Abstract: Sampling variance smoothing is an important topic in small area estimation. In this article, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis and simulation study indicate that the proposed sampling variance smoothing methods perform very well for small area estimation.

Keywords: Coefficient of variation; design effect; generalized variance function; log-linear model; relative error (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2023-0026 (text/html)

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:vrs:offsta:v:39:y:2023:i:4:p:571-590:n:5

DOI: 10.2478/jos-2023-0026

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().