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Hierarchical Bayes small‐area estimation with an unknown link function.
- Source :
- Scandinavian Journal of Statistics; Sep2019, Vol. 46 Issue 3, p885-897, 13p
- Publication Year :
- 2019
-
Abstract
- Area‐level unmatched sampling and linking models have been widely used as a model‐based method for producing reliable estimates of small‐area means. However, one practical difficulty is the specification of a link function. In this paper, we relax the assumption of a known link function by not specifying its form and estimating it from the data. A penalized‐spline method is adopted for estimating the link function, and a hierarchical Bayes method of estimating area means is developed using a Markov chain Monte Carlo method for posterior computations. Results of simulation studies comparing the proposed method with a conventional approach based on a known link function are presented. In addition, the proposed method is applied to data from the Survey of Family Income and Expenditure in Japan and poverty rates in Spanish provinces. [ABSTRACT FROM AUTHOR]
- Subjects :
- HIERARCHICAL Bayes model
BAYES' estimation
MONTE Carlo method
Subjects
Details
- Language :
- English
- ISSN :
- 03036898
- Volume :
- 46
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Scandinavian Journal of Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 137924789
- Full Text :
- https://doi.org/10.1111/sjos.12376