Title: Generalized least squares can overcome the critical threshold in respondent-driven sampling,
Abstract: In order 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 $O(n^{-1})$, 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
$O(n^{-1})$. We then derive two classes of feasible GLS estimators. The first
class is based upon a Degree Corrected Stochastic Blockmodel for the underlying
social network. The second class is based upon a rank-two model. It might be of
independent interest that in both model classes, the theoretical results show
that it is possible to estimate the spectral properties of the population
network from the sampled observations. Simulations on empirical social networks
show that the feasible GLS (fGLS) estimators can have drastically smaller error
and rarely increase the error. A diagnostic plot helps to identify where fGLS
will aid estimation. The fGLS estimators continue to outperform standard
estimators even when they are built from a misspecified model and when there is
preferential recruitment.