Seminar Details

Seminar Details


Thursday

Jul 31

10:00 am

Inference in Network-Based Respondent-Driven Sampling

Krista Gile

Final Exam

University of Washington - Department of Statistics

Respondent-Driven Sampling employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample.

Current estimation focuses on estimating population averages in the hard-to-reach population. These estimates are based on strong assumptions allowing the sample to be treated as a probability sample. In particular, the current estimator assumes a with-replacement sample or small sample fraction, while in practice samples are without-replacement, and often include a large fraction of the population. We present a simulation study illustrating the sensitivity of the current estimator to violations of these assumptions.

We introduce a new estimator which allows for the without-replacement structure of the sample, and demonstrate its superior performance in cases where the sample fraction is large.