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MULTIPLE‐RECORD SYSTEMS ESTIMATION USING LATENT CLASS MODELS
Author(s) -
Wang Yan,
Thandrayen Joanne
Publication year - 2009
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2008.00531.x
Subject(s) - identifiability , latent class model , estimation , econometrics , class (philosophy) , statistics , mathematics , population , maximum likelihood , expectation–maximization algorithm , geography , computer science , artificial intelligence , medicine , environmental health , management , economics
Summary Capture–recapture methods (also referred to as ‘multiple‐record systems’) have been widely used in enumerating human populations in the fields of epidemiology and public health. In this article, we introduce latent class models into multiple‐record systems to account for unobserved heterogeneity in the population. Two approaches, the full and the conditional likelihood, are proposed to estimate the unknown population abundance. We also suggest rules to diagnose identifiability of the proposed latent class models. The methodologies are illustrated by two real examples: the first is to count the undercount of homelessness in the Adelaide central business district, and the second concerns the incidence of diabetes in a small Italian town.