Combining cluster sampling and link-tracing sampling to estimate the size of a hidden population: asymptotic properties of the estimators
Martin H. F\'elix Medina

TL;DR
This paper analyzes the asymptotic properties of estimators for hidden population size using combined cluster and link-tracing sampling, extending previous models to account for heterogeneity in link probabilities.
Contribution
It establishes conditions under which the maximum likelihood estimators are consistent and asymptotically normal, considering heterogeneity in link-probabilities.
Findings
Both unconditional and conditional MLEs are consistent.
The estimators have different asymptotic normal distributions.
Conditions for model validity are provided.
Abstract
F\'elix-Medina and Thompson (2004) proposed a variant of link-tracing sampling to estimate the size of a hidden population such as drug users, sexual workers or homeless people. In their variant a sampling frame of sites where the members of the population tend to gather is constructed. The frame is not assumed to cover the whole population, but only a portion of it. A simple random sample of sites is selected; the people in the sampled sites are identified and are asked to name other members of the population which are added to the sample. Those authors proposed maximum likelihood estimators of the population size which derived from a multinomial model for the numbers of people found in the sampled sites and a model that considers that the probability that a person is named by any element in a particular sampled site (link-probability) does not depend on the named person, that is, that…
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Taxonomy
TopicsHIV, Drug Use, Sexual Risk · Census and Population Estimation · Data-Driven Disease Surveillance
