Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles
Metadatos
Afficher la notice complèteEditorial
MDPI
Materia
Nonprobability surveys Propensity score adjustment Survey sampling Poverty measures
Date
2022-12-12Referencia bibliográfica
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]
Patrocinador
Spanish 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-UGR20Résumé
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.