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A Bayesian Model for Estimating Population Means Using a Link‐Tracing Sampling Design
Author(s) -
St. Clair Katherine,
O'Connell Daniel
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01631.x
Subject(s) - computer science , sampling design , bayesian probability , sampling (signal processing) , statistics , link (geometry) , population , data mining , mathematics , artificial intelligence , medicine , filter (signal processing) , computer vision , computer network , environmental health
Summary Link‐tracing sampling designs can be used to study human populations that contain “hidden” groups who tend to be linked together by a common social trait. These links can be used to increase the sampling intensity of a hidden domain by tracing links from individuals selected in an initial wave of sampling to additional domain members. Chow and Thompson (2003, Survey Methodology 29 , 197–205) derived a Bayesian model to estimate the size or proportion of individuals in the hidden population for certain link‐tracing designs. We propose an addition to their model that will allow for the modeling of a quantitative response. We assess properties of our model using a constructed population and a real population of at‐risk individuals, both of which contain two domains of hidden and nonhidden individuals. Our results show that our model can produce good point and interval estimates of the population mean and domain means when our population assumptions are satisfied.