On overall sampling plan for small area estimation
Abstract
The time and budget restrictions in survey sampling can impose limits on the area sample sizes.
This may reduce the possibility to obtain area-specific and population parameters estimates
with adequate precision. Market research companies and institutes for producing official
statistics face frequently this problem. Various models and methods for small area estimation
(SAE) have been developed to solve this problem. The sample allocation must support the
selected model and method to ensure efficient estimation and must be implemented in the
design phase of the survey. The proposed allocation is developed by incorporating auxiliary
information, a model, and an estimation method. The estimated parameters are area and
population totals. The performance of this allocation is assessed through design-based
simulation experiments using real, regularly collected register data. Five other allocations
selected from the literature serve as references. Model-based estimation is applied to two
allocations and design-based Horvitz-Thompson and model-assisted GREG estimation to four
model-free allocations. Four allocations are based on past register data. The allocation with
uniquely best performance among all alternatives was not found, but the simulation study
supports the comprehensive survey plan where the sampling design is conditioned on the
available auxiliary information, selected model, and method.
Main Authors
Format
Articles
Research article
Published
2017
Series
Subjects
Publication in research information system
Publisher
IOS Press
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201805082502Use this for linking
Review status
Peer reviewed
ISSN
1874-7655
DOI
https://doi.org/10.3233/SJI-170370
Language
English
Published in
Statistical Journal of the IAOS
Citation
- Keto, M., & Pahkinen, E. (2017). On overall sampling plan for small area estimation. Statistical Journal of the IAOS, 33(3), 727-740. https://doi.org/10.3233/SJI-170370
Copyright© IOS Press, 2017.