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A latent variable regression model for capture-recapture data

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  • Thandrayen, Joanne
  • Wang, Yan

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.

Suggested Citation

  • Thandrayen, Joanne & Wang, Yan, 2009. "A latent variable regression model for capture-recapture data," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2740-2746, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2740-2746
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    References listed on IDEAS

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    1. Guan-Hua Huang & Karen Bandeen-Roche, 2004. "Building an identifiable latent class model with covariate effects on underlying and measured variables," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 5-32, March.
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    5. Robert M. Dorazio & J. Andrew Royle, 2003. "Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals," Biometrics, The International Biometric Society, vol. 59(2), pages 351-364, June.
    6. Bartolucci, Francesco & Forcina, Antonio, 2006. "A Class of Latent Marginal Models for CaptureRecapture Data With Continuous Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 786-794, June.
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