Respondent-driven sampling as Markov Chain Monte Carlo
Respondent-driven sampling as Markov Chain Monte Carlo
Description
Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. The sample is collected through a form of snowball sampling where current sample members recruit future sample members. In this paper we observe that respondent-driven sampling can be viewed as Markov Chain Monte Carlo (MCMC) importance sampling. By establishing this connection, we are able to draw on the MCMC literature to address key RDS implementation and analysis issues.
People
- Principal investigators: Matt Salganik, Sharad Goel
- Researcher: Cyrus Samii