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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. 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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    5. Shirley Pledger, 2000. "Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures," Biometrics, The International Biometric Society, vol. 56(2), pages 434-442, June.
    6. Peter G.M. Van Der Heijden & Maarten Cruyff & Hans C. Van Houwelingen, 2003. "Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(3), pages 289-304, August.
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