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
Keywords: network scale-up method; aggregated relational data; robust estimators; adaptive procedures; M-estimators
Scheduled
AMC2 Robust Methods
June 10, 2025 5:10 PM
Auditorio 1. Ricard Vinyes