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MODELING THE VISIBILITY DISTRIBUTION FOR RESPONDENT-DRIVEN SAMPLING WITH APPLICATION TO POPULATION SIZE ESTIMATION.

Katherine R McLaughlin1, Lisa G Johnston2, Xhevat Jakupi3

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The Annals of Applied Statistics
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) can improve hidden population estimates by using a new "visibility" model. This approach addresses biases from self-reported network sizes, enhancing population size and prevalence estimations.

Keywords:
Heaped datahidden populationmeasurement error modelmodel-based survey samplingnetwork sampling

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Area of Science:

  • Statistics
  • Epidemiology
  • Social Network Analysis

Background:

  • Respondent-driven sampling (RDS) is crucial for studying hidden populations but relies on self-reported network sizes, which are prone to bias.
  • Current RDS estimators approximate inclusion probabilities using self-reported network size (degree), leading to potential inaccuracies.

Purpose of the Study:

  • To enhance the successive sampling population size estimation (SS-PSE) framework for RDS data.
  • To introduce a "visibility" measurement error model to replace unreliable self-reported network sizes.
  • To improve the accuracy of population size and prevalence estimations from RDS.

Main Methods:

  • Developed an enhanced SS-PSE framework incorporating a measurement error model for participant "visibility."
  • Modeled the number of individuals a participant can recruit.
  • Applied the visibility SS-PSE framework to RDS data from three populations in Kosovo.

Main Results:

  • The visibility model effectively smooths degree distributions and handles missing/invalid network size data.
  • Demonstrated the performance of the enhanced SS-PSE framework on real-world RDS data.
  • Inferred visibilities provide a more robust measure than self-reported network sizes.

Conclusions:

  • The proposed visibility modeling framework offers a significant improvement over traditional SS-PSE methods for RDS.
  • This approach can mitigate biases associated with self-reported network sizes in hidden population studies.
  • The framework shows potential for extension to prevalence estimation in future research.