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2016 | 17 | 1 | 133-154

Article title

A Comparison of Small Area and Calibration Estimators via Simulation

Content

Title variants

Languages of publication

EN

Abstracts

EN
Domain estimates are typically obtained using calibration estimators that are direct or modified direct. They are direct if they strictly use data within the domain of interest. They are modified direct if they use both data within and outside the domain of interest. An alternative way of producing these estimates is through small area procedures. In this article, we compare the performance of these two approaches via a simulation. The population is generated using a hierarchical model that includes both area effects and unit level random errors. The population is made up of mutually exclusive domains of different sizes, ranging from a small number of units to a large number of units. We select many independent simple random samples of fixed size from the population and compute various estimates for each sample using the available auxiliary information. The estimates computed for the simulation included the Horvitz-Thompson estimator, the synthetic estimator (indirect estimate), calibration estimators, and unit level based estimators (small area estimate). The performance of these estimators is summarized based on their design- based properties

Year

Volume

17

Issue

1

Pages

133-154

Physical description

Contributors

  • Statistical Research and Innovation Division, Statistics Canada, 16 D, R.-H.-Coats Building, Ottawa, Ontario, Canada, K1A 0T6.
  • Statistical Research and Innovation Division, Statistics Canada, 16 D, R.-H.-Coats Building, Ottawa, Ontario, Canada, K1A 0T6.

References

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  • ESTEVAO, V., HIDIROGLOU, M. A., YOU, Y., (2014). Methodology Software Library - Small area Estimation Methodology Specifications for Area and Unit Level based Models. Technical Report, Statistics Canada.
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  • PFEFFERMANN, D., SVERCHKOV, M., (2007). Small Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas. Journal of the American Statistical Association 102 (480), 1427–1439.
  • RAO, J. N. K., (2003). Small Area Estimation: John Wiley & Sons.
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  • SINGH, M. P., GAMBINO, J., MANTEL, H., (1994). Issues and Strategies for Small Area Data. Survey Methodology, 20 (1), 3–22.
  • WOODRUFF, R. S., (1966). Use of a Regression Technique to Produce Area Breakdowns of the Monthly National Estimates of Retail Trade. Journal of the American Statistical Association, 61 (314), 496–504.
  • YOU, Y., RAO, J. N. K., (2002). A pseudo-empirical best linear unbiased prediction approach to small area estimation using survey weights, Canadian Journal of Statistics, 30, 431–439.
  • VERRET, F., RAO, J. N. K., VERRET, F., RAO, J. N. K., HIDIROGLOU, M. A., (2015). Model-based small area estimation under informative sampling. To appear in the December 2015 issue of Survey Methodology.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-86dbfd62-682a-475b-85ff-40fc854b9673
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