Generalized least squares can overcome the critical threshold in respondent-driven sampling.

ABSTRACT: To sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov model for RDS, previous research has shown that if the typical participant refers too many contacts, then the variance of common estimators does not decay like [Formula: see text], where n is the sample size. This implies that confidence intervals will be far wider than under a typical sampling design. Here we show that generalized least squares (GLS) can effectively reduce the variance of RDS estimates. In particular, a theoretical analysis indicates that the variance of the GLS estimator is [Formula: see text] We then derive two classes of feasible GLS estimators. The first class is based upon a Degree Corrected Stocha

SUBMITTER: Roch S 

PROVIDER: S-EPMC6187121 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature