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An approximation to the optimal subsample allocation for small areas

W. B. Molefe (), D. K. Shangodoyin () and R. G. Clark ()

Statistics in Transition new series, 2015, vol. 16, issue 2, 163-182

Abstract: This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.

Keywords: composite estimation; mean squared error; sample design; small area estimation; sample size allocation; Taylor approximation (search for similar items in EconPapers)
Date: 2015
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Handle: RePEc:csb:stintr:v:16:y:2015:i:2:p:163-182