S. Díaz Aranda, J. M. Ramírez, J. Aguilar, A. Fernández Anta, R. E. Lillo
The Network Scale-up Method (NSUM) is a relatively recent survey-based approach for estimating hard-to-reach populations based on the aggregate data of the respondents’ acquaintances. However, NSUM is prone to biases arising from participant behavior, particularly due to sensitivity to small deviation because of the reliance on sample means. This work investigates the use of eight robust proposals for the two classical NSUM to mitigate errors caused by misreporting, contamination, barrier effects, prevalence, skewness, and tail length. We evaluate our proposals through simulation experiments using synthetic networks that mimic real-world social structures, as well as real data. The results demonstrate that classical NSUM estimators perform poorly in contaminated settings. While robust methods generally improve performance, some degrade under barrier effects. Additionally, distortions caused by low prevalence significantly influence the selection of the most effective robust estimator
Palabras clave: network scale-up method; aggregated relational data; robust estimators; adaptive procedures; M-estimators
Programado
AMC2 Métodos Robustos
10 de junio de 2025 17:10
Auditorio 1. Ricard Vinyes