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Bias Calibration for Robust Estimation in Small Areas

In: Robust and Multivariate Statistical Methods

Author

Listed:
  • Setareh Ranjbar

    (University of Lausanne, HEC)

  • Elvezio Ronchetti

    (University of Geneva, Research Center for Statistics and Geneva School of Economics and Management)

  • Stefan Sperlich

    (University of Geneva, Geneva School of Economics and Management)

Abstract

It is well known that the existence of outliers in a sample can significantly affect the estimation of population parameters. Intuition suggests that this is even more the case in the context of small area estimation. If influential, outliers may heavily affect parameter estimates for areas in which they occur, especially when the domain-sample size is tiny. An obvious remedy is to use robust estimators but with the drawback of a potential bias. We compare different approaches, including some new ones, for bias calibration in this context. Among other findings, the simulations indicate that the new proposals can lead to more efficient estimators compared to existing methods. We conclude the study applying our estimators to obtain Gini coefficients in labor market areas of the Tuscany region of Italy. The new methods reveal a different picture than existing methods. We extend our ideas to predictions for non-sampled areas.

Suggested Citation

  • Setareh Ranjbar & Elvezio Ronchetti & Stefan Sperlich, 2023. "Bias Calibration for Robust Estimation in Small Areas," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 365-394, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-22687-8_17
    DOI: 10.1007/978-3-031-22687-8_17
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