A Bayesian benchmarking of the Scott-Smith model for small areas
When the finite population 'totals' are estimated for individual areas, they do not necessarily add up to the known 'total' for all areas. Benchmarking (BM) is a technique used to ensure that the totals for all areas match the grand total, which can be obtained from an independen...
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Published in | Journal of statistical computation and simulation Vol. 81; no. 11; pp. 1593 - 1608 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis
01.11.2011
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | When the finite population 'totals' are estimated for individual areas, they do not necessarily add up to the known 'total' for all areas. Benchmarking (BM) is a technique used to ensure that the totals for all areas match the grand total, which can be obtained from an independent source. BM is desirable to practitioners of survey sampling. BM shifts the small-area estimators to accommodate the constraint. In doing so, it can provide increased precision to the small-area estimators of the finite population means or totals. The Scott-Smith model is used to benchmark the finite population means of small areas. This is a one-way random effects model for a superpopulation, and it is computationally convenient to use a Bayesian approach. We illustrate our method by estimating body mass index using data in the third National Health and Nutrition Examination Survey. Several properties of the benchmarked small-area estimators are obtained using a simulation study. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0094-9655 1563-5163 |
DOI: | 10.1080/00949655.2010.496726 |