- Bayesian Anal.
- Volume 12, Number 3 (2017), 729-752.
A Generalised Semiparametric Bayesian Fay–Herriot Model for Small Area Estimation Shrinking Both Means and Variances
In survey sampling, interest often lies in unplanned domains (or small areas), whose sample sizes may be too small to allow for accurate design-based inference. To improve the direct estimates by borrowing strength from similar domains, most small area methods rely on mixed effects regression models.
This contribution extends the well known Fay–Herriot model (Fay and Herriot, 1979) within a Bayesian approach in two directions. First, the default normality assumption for the random effects is replaced by a nonparametric specification using a Dirichlet process. Second, uncertainty on variances is explicitly introduced, recognizing the fact that they are actually estimated from survey data. The proposed approach shrinks variances as well as means, and accounts for all sources of uncertainty. Adopting a flexible model for the random effects allows to accommodate outliers and vary the borrowing of strength by identifying local neighbourhoods where the exchangeability assumption holds. Through application to real and simulated data, we investigate the performance of the proposed model in predicting the domain means under different distributional assumptions. We also focus on the construction of credible intervals for the area means, a topic that has received less attention in the literature. Frequentist properties such as mean squared prediction error (MSPE), coverage and interval length are investigated. The experiments performed seem to indicate that inferences under the proposed model are characterised by smaller mean squared error than competing approaches; frequentist coverage of the credible intervals is close to nominal.
Bayesian Anal., Volume 12, Number 3 (2017), 729-752.
First available in Project Euclid: 7 September 2016
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Polettini, Silvia. A Generalised Semiparametric Bayesian Fay–Herriot Model for Small Area Estimation Shrinking Both Means and Variances. Bayesian Anal. 12 (2017), no. 3, 729--752. doi:10.1214/16-BA1019. https://projecteuclid.org/euclid.ba/1473276257