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Capture–recapture estimation based upon the geometric distribution allowing for heterogeneity

Author

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  • Sa-aat Niwitpong
  • Dankmar Böhning
  • Peter Heijden
  • Heinz Holling

Abstract

Capture–Recapture methods aim to estimate the size of an elusive target population. Each member of the target population carries a count of identifications by some identifying mechanism—the number of times it has been identified during the observational period. Only positive counts are observed and inference needs to be based on the observed count distribution. A widely used assumption for the count distribution is a Poisson mixture. If the mixing distribution can be described by an exponential density, the geometric distribution arises as the marginal. This note discusses population size estimation on the basis of the zero-truncated geometric (a geometric again itself). In addition, population heterogeneity is considered for the geometric. Chao’s estimator is developed for the mixture of geometric distributions and provides a lower bound estimator which is valid under arbitrary mixing on the parameter of the geometric. However, Chao’s estimator is also known for its relatively large variance (if compared to the maximum likelihood estimator). Another estimator based on a censored geometric likelihood is suggested which uses the entire sample information but is less affected by model misspecifications. Simulation studies illustrate that the proposed censored estimator comprises a good compromise between the maximum likelihood estimator and Chao’s estimator, e.g. between efficiency and bias. Copyright Springer-Verlag 2013

Suggested Citation

  • Sa-aat Niwitpong & Dankmar Böhning & Peter Heijden & Heinz Holling, 2013. "Capture–recapture estimation based upon the geometric distribution allowing for heterogeneity," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(4), pages 495-519, May.
    • Handle: RePEc:spr:metrik:v:76:y:2013:i:4:p:495-519
    DOI: 10.1007/s00184-012-0401-0
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    References listed on IDEAS

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    1. John M. Roberts & Devon D. Brewer, 2006. "Estimating the prevalence of male clients of prostitute women in Vancouver with a simple capture–recapture method," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 745-756, October.
    2. 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.
    3. William A. Link, 2003. "Nonidentifiability of Population Size from Capture-Recapture Data with Heterogeneous Detection Probabilities," Biometrics, The International Biometric Society, vol. 59(4), pages 1123-1130, December.
    4. Wang, Ji-Ping Z. & Lindsay, Bruce G., 2005. "A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 942-959, September.
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    Cited by:

    1. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2019. "On the Turing estimator in capture–recapture count data under the geometric distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 149-172, March.
    2. Jiménez-Gamero, M.D. & Alba-Fernández, M.V., 2021. "A test for the geometric distribution based on linear regression of order statistics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 103-123.
    3. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2017. "Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 49-79, March.
    4. Ryan T. Godwin & Dankmar Böhning, 2017. "Estimation of the population size by using the one-inflated positive Poisson model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 425-448, February.

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