Técnicas de Machine Learning para inferencia en muestras no probabilísticas
L. Castro Martín, R. Ferri García, M. M. Rueda García
La comodidad de las encuestas online ha supuesto un importante aumento en su popularidad a la hora de recolectar datos. Sin embargo, normalmente no siguen una metodología probabilística. Las muestras no-probabilísticas implican importantes problemas de sesgo, cobertura y falta de respuesta que invalidan la fiabilidad de los resultados obtenidos. Algunos métodos han sido propuestos para corregir estos problemas, como Propensity Score Adjustment o Statistical Matching. En este trabajo hacemos un recorrido a través de las diversas técnicas del estado del arte del Machine Learning que pueden integrarse con dichos métodos para mejorar significativamente las estimaciones obtenidas.
Keywords: machine learning, muestreo, no-probabilístico
Scheduled
Invited Session Data Analysis and Social Science. Data integration probabilistic, non-probabilistic surveys and big data
June 7, 2022 3:30 PM
A11
Other papers in the same session
M. M. Rueda García, L. Castro, B. Cobo Rodríguez, R. Ferri García, S. Pasadas del Amo
B. Cobo Rodríguez, M. M. Rueda García, L. Calle López, F. López Torrecillas
R. Ferri García, J. Beaumont, K. Bosa, J. Charlebois, K. Chu
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