Estimating the Size and Distribution of Networked Populations with Snowball Sampling. (17th July 2021)

• Record Type: Journal Article
• Authors: Vincent, Kyle; Thompson, Steve
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 10:Number 2(2022)
• Page Start: 397
• Page End: 418
• Publication Date: 2021-07-17
• Subjects: Bayesian inference -- Ignoring unit labels -- Markov chain Monte Carlo -- Missing data -- Multiple imputation; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smaa042
• Languages: English
• ISSNs: 2325-0984

Abstract

Abstract: A new strategy is introduced for estimating population size and networked population characteristics. Sample selection is based on a multi-wave snowball sampling design. A generalized stochastic block model is posited for the population's network graph. Inference is based on a Bayesian data augmentation procedure. Applications are provided to simulated populations and an empirical population. The results demonstrate that statistically efficient estimates of the size and distribution of the population can be achieved.