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Parametric and semiparametric models for recapture and removal studies: a likelihood approach

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  • Kani Chen

Abstract

Capture–recapture processes are biased samplings of recurrent event processes, which can be modelled by the Andersen–Gill intensity model. The intensity function is assumed to be a function of time, covariates and a parameter. We derive the maximum likelihood estimators of both the parameter and the population size and show the consistency and asymptotic normality of the estimators for both recapture and removal studies. The estimators are asymptotically efficient and their theoretical asymptotic relative efficiencies with respect to the existing estimators of Yip and co‐workers can be as large as ∞. The variance estimation and a numerical example are also presented.

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  • Kani Chen, 2001. "Parametric and semiparametric models for recapture and removal studies: a likelihood approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 607-619.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:3:p:607-619
    DOI: 10.1111/1467-9868.00302
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    Cited by:

    1. Paul S. F. Yip & Hua-Zhen Lin & Liqun Xi, 2005. "A Semiparametric Method for Estimating Population Size for Capture–Recapture Experiments with Random Covariates in Continuous Time," Biometrics, The International Biometric Society, vol. 61(4), pages 1085-1092, December.
    2. Li, Haoqi & Lin, Huazhen & Yip, Paul S.F. & Li, Yuan, 2019. "Estimating population size of heterogeneous populations with large data sets and a large number of parameters," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 34-44.
    3. Jing Cheng & Jing Qin & Biao Zhang, 2009. "Semiparametric estimation and inference for distributional and general treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 881-904, September.
    4. Jakub Stoklosa & Wen-Han Hwang & Sheng-Hai Wu & Richard Huggins, 2011. "Heterogeneous Capture–Recapture Models with Covariates: A Partial Likelihood Approach for Closed Populations," Biometrics, The International Biometric Society, vol. 67(4), pages 1659-1665, December.

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