Estimating the Size and Distribution of Networked Populations with Snowball Sampling

• Activity Number: 317
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Kyle Vincent* and Steve Thompson
• Companies: Bank of Canada and Simon Fraser University
• Keywords: Bayesian inference ; Ignoring unit labels ; Markov chain Monte Carlo ; Missing data ; Multiple imputation

Abstract

A new strategy is introduced for estimating networked population characteristics. Selection is based on the complete one-wave snowball sampling design, and a generalized stochastic block graph model serves to model the network topology. Inference is based on the use of a Bayesian data augmentation procedure. This procedure has the advantage over existing methods in that it can be applied to a networked population of unknown size. An application is provided to a study of an empirical population at risk for HIV/AIDS. The results demonstrate that efficient estimates of the size and distribution of the population can be achieved with the novel strategy.