Inferencia en encuestas no probabilísticas con Kernel Weighting Method.
J. L. Rueda Sánchez, R. Ferri García, M. M. Rueda García
Debido a la importancia en los cuestionarios online y los big data, las muestras no probabilísticas han adquirido una gran importancia. Pero debido a su naturaleza, las estimaciones basadas en datos obtenidos a partir de estas muestras adolecen de diversos sesgos, destacando el sesgo de voluntariedad, y pueden dar lugar a estimaciones imprecisas.
En este trabajo estudiaremos nuevas técnicas de reducción de sesgos de no voluntariedad, y nos centraremos en la técnica Kernel Weighting (KW) por su innovador procedimiento y por su posible mejor rendimiento a la hora de minimizar sesgos. Mediante un estudio de simulación compararemos el comportamiento de KW con otra técnica ya ampliamente estudiada y comprobada su eficacia como es el Propensity Score Adjustment (PSA). Nuestro estudio muestra que a la hora de reducir el sesgo los estimadores KW son bastante mejores, mientras que el Error Cuadrático Medio en ambas técnicas tiene un comportamiento similar.
Keywords: Sesgos de no voluntariedad, Encuestas no probabilísticas, Kernel Weighting methods, Propensity Score Adjustment, Muestreo no probabilístico
Scheduled
Posters IV
June 10, 2022 10:10 AM
Faculty Hall
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