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A Comparison of Small Area and Calibration Estimators Via Simulation

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  • Hidiroglou M. A.

    (Statistical Research and Innovation Division, Statistics Canada, 16 D, R.-H.-Coats Building, Ottawa, Ontario, Canada)

  • Estevao V. M.

    (Statistical Research and Innovation Division, Statistics Canada, 16 D, R.-H.-Coats Building, Ottawa, Ontario, Canada)

Abstract

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.

Suggested Citation

  • Hidiroglou M. A. & Estevao V. M., 2016. "A Comparison of Small Area and Calibration Estimators Via Simulation," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 133-154, March.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:1:p:133-154:n:7
    DOI: 10.21307/stattrans-2016-009
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    References listed on IDEAS

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    1. Pfeffermann, Danny & Sverchkov, Michail, 2007. "Small-Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1427-1439, December.
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