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On Estimating Quantiles Using Auxiliary Information

Author

Listed:
  • Berger Yves G.

    (Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, SO17 1BJ, UK)

  • Munoz Juan F.

    (Department of Quantitative Methods in Economics and Business, University of Granada, Granada, 18071, Spain)

Abstract

We propose a transformation-based approach for estimating quantiles using auxiliary information. The proposed estimators can be easily implemented using a regression estimator. We show that the proposed estimators are consistent and asymptotically unbiased. The main advantage of the proposed estimators is their simplicity. Despite the fact the proposed estimators are not necessarily more efficient than their competitors, they offer a good compromise between accuracy and simplicity. They can be used under single and multistage sampling designs with unequal selection probabilities. A simulation study supports our finding and shows that the proposed estimators are robust and of an acceptable accuracy compared to alternative estimators, which can be more computationally intensive.

Suggested Citation

  • Berger Yves G. & Munoz Juan F., 2015. "On Estimating Quantiles Using Auxiliary Information," Journal of Official Statistics, Sciendo, vol. 31(1), pages 101-119, March.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:1:p:101-119:n:5
    DOI: 10.1515/jos-2015-0005
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    References listed on IDEAS

    as
    1. Vijay Verma & Gianni Betti, 2011. "Taylor linearization sampling errors and design effects for poverty measures and other complex statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1549-1576, August.
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