A latent variable regression model for capture-recapture data

Thandrayen, J and Wang, Y 2009, 'A latent variable regression model for capture-recapture data', Computational Statistics and Data Analysis, vol. 53, no. 7, pp. 2740-2746.

Document type: Journal Article
Collection: Journal Articles

Title A latent variable regression model for capture-recapture data
Author(s) Thandrayen, J
Wang, Y
Year 2009
Journal name Computational Statistics and Data Analysis
Volume number 53
Issue number 7
Start page 2740
End page 2746
Total pages 6
Publisher Elsevier Science Bv
Abstract Capture–recapture methods are used to estimate the prevalence of diseases in the field of epidemiology. The information used for estimation purposes are available from multiple lists, whereby giving rise to the problems of list dependence and heterogeneity. In this paper, modelling is focused on the heterogeneity part. We present a new binomial latent class model which takes into account both the observed and unobserved heterogeneity within capture–recapture data. We adopt the conditional likelihood approach and perform estimation via the EM algorithm. We also derive the mathematical expressions for the computation of the standard error of the unknown population size. An application to data on diabetes patients in a town in northern Italy is discussed.
Subject Biostatistics
DOI - identifier 10.1016/j.csda.2009.01.014
Copyright notice © 2009 Elsevier B.V. All rights reserved.
ISSN 0167-9473
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
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