Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles
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Nonprobability surveysPropensity score adjustmentSurvey samplingPoverty measures
Rueda, M.d.M.; Martínez-Puertas, S.; Castro-Martín, L. Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles. Mathematics 2022, 10, 4726. [https://doi.org/10.3390/math10244726]
SponsorshipSpanish Government PID2019-106861RB-I00; IMAG-Maria de Maeztu CEX2020-001105-M/AEI/10.13039/501100011033; FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades FQM170-UGR20
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.