Estimating hidden population size from a single respondent-driven sampling survey
arxiv(2024)
摘要
This work is concerned with the estimation of hard-to-reach population sizes
using a single respondent-driven sampling (RDS) survey, a variant of
chain-referral sampling that leverages social relationships to reach members of
a hidden population. The popularity of RDS as a standard approach for surveying
hidden populations brings theoretical and methodological challenges regarding
the estimation of population sizes, mainly for public health purposes. This
paper proposes a frequentist, model-based framework for estimating the size of
a hidden population using a network-based approach. An optimization algorithm
is proposed for obtaining the identification region of the target parameter
when model assumptions are violated. We characterize the asymptotic behavior of
our proposed methodology and assess its finite sample performance under
departures from model assumptions.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要