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Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles

Author

Listed:
  • María del Mar Rueda

    (Department of Statistics and O.R. and Institute of Mathematics, University of Granada, 18071 Granada, Spain)

  • Sergio Martínez-Puertas

    (Department of Mathematics, University of Almería, 04120 Almería, Spain)

  • Luis Castro-Martín

    (Andalusian School of Public Health, University of Granada, 18011 Granada, Spain)

Abstract

Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target population. Techniques to mitigate this selection bias in non-probability samples often involve calibration, propensity score adjustment, or statistical matching. In this article, we consider the problem of estimating the finite population distribution function in the context of non-probability surveys and show how some methodologies formulated for linear parameters can be adapted to this functional parameter, both theoretically and empirically, thus enhancing the accuracy and efficiency of the estimates made.

Suggested Citation

  • María del Mar Rueda & Sergio Martínez-Puertas & Luis Castro-Martín, 2022. "Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4726-:d:1001192
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    References listed on IDEAS

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