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Parametric regression models for continuous time removal and recapture studies

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  • D. Y. Lin
  • P. S. F. Yip

Abstract

We use a class of parametric counting process regression models that are commonly employed in the analysis of failure time data to formulate the subject‐specific capture probabilities for removal and recapture studies conducted in continuous time. We estimate the regression parameters by modifying the conventional likelihood score function for left‐truncated and right‐censored data to accommodate an unknown population size and missing covariates on uncaptured subjects, and we subsequently estimate the population size by a martingale‐based estimating function. The resultant estimators for the regression parameters and population size are consistent and asymptotically normal under appropriate regularity conditions. We assess the small sample properties of the proposed estimators through Monte Carlo simulation and we present an application to a bird banding exercise.

Suggested Citation

  • D. Y. Lin & P. S. F. Yip, 1999. "Parametric regression models for continuous time removal and recapture studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 401-411, April.
    • Handle: RePEc:bla:jorssb:v:61:y:1999:i:2:p:401-411
    DOI: 10.1111/1467-9868.00184
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    Cited by:

    1. Paul S. F. Yip & Yan Wang, 2002. "A Unified Parametric Regression Model for Recapture Studies with Random Removals in Continuous Time," Biometrics, The International Biometric Society, vol. 58(1), pages 192-199, March.
    2. 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.
    3. Yang Liu & Yukun Liu & Pengfei Li & Lin Zhu, 2021. "Maximum likelihood abundance estimation from capture‐recapture data when covariates are missing at random," Biometrics, The International Biometric Society, vol. 77(3), pages 1050-1060, September.
    4. Linda Altieri & Alessio Farcomeni & Danilo Alunni Fegatelli, 2023. "Continuous time‐interaction processes for population size estimation, with an application to drug dealing in Italy," Biometrics, The International Biometric Society, vol. 79(2), pages 1254-1267, June.
    5. Ying Xu & Liping Liu & Na You & Hungyu Pan & Paul Yip, 2007. "Estimating Population Size for a Continuous Time Frailty Model with Covariates in a Capture–Recapture Study," Biometrics, The International Biometric Society, vol. 63(3), pages 917-921, September.

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