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Continuous†time capture–recapture in closed populations

Author

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  • Matthew R. Schofield
  • Richard J. Barker
  • Nicholas Gelling

Abstract

The standard approach to fitting capture–recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. We show how continuous†time models can be fitted as easily as discrete†time alternatives. The likelihood is factored so that efficient Markov chain Monte Carlo algorithms can be implemented for Bayesian estimation, available online in the R package ctime. We consider goodness†of†fit tests for behavior and heterogeneity effects as well as implementing models that allow for such effects.

Suggested Citation

  • Matthew R. Schofield & Richard J. Barker & Nicholas Gelling, 2018. "Continuous†time capture–recapture in closed populations," Biometrics, The International Biometric Society, vol. 74(2), pages 626-635, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:626-635
    DOI: 10.1111/biom.12763
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    References listed on IDEAS

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    1. repec:bla:biomet:v:71:y:2015:i:4:p:1070-1080 is not listed on IDEAS
    2. 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.
    3. Chris Hans, 2009. "Bayesian lasso regression," Biometrika, Biometrika Trust, vol. 96(4), pages 835-845.
    4. R. King & S. P. Brooks, 2008. "On the Bayesian Estimation of a Closed Population Size in the Presence of Heterogeneity and Model Uncertainty," Biometrics, The International Biometric Society, vol. 64(3), pages 816-824, September.
    5. Richard J. Barker & Matthew R. Schofield & Janine A. Wright & Alain C. Frantz & Chris Stevens, 2014. "Closed-population capture–recapture modeling of samples drawn one at a time," Biometrics, The International Biometric Society, vol. 70(4), pages 775-782, December.
    6. Janine A. Wright & Richard J. Barker & Matthew R. Schofield & Alain C. Frantz & Andrea E. Byrom & Dianne M. Gleeson, 2009. "Incorporating Genotype Uncertainty into Mark–Recapture-Type Models For Estimating Abundance Using DNA Samples," Biometrics, The International Biometric Society, vol. 65(3), pages 833-840, September.
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    Citations

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    Cited by:

    1. Wei Zhang & Simon J. Bonner, 2020. "On continuous‐time capture‐recapture in closed populations," Biometrics, The International Biometric Society, vol. 76(3), pages 1028-1033, September.
    2. Kenneth F. Kellner & Arielle W. Parsons & Roland Kays & Joshua J. Millspaugh & Christopher T. Rota, 2022. "A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 321-338, June.
    3. 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.
    4. Paul McLaughlin & Haim Bar, 2021. "A spatial capture–recapture model with attractions between individuals," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    5. Matthew R. Schofield & Richard J. Barker, 2020. "Rejoinder to “On continuous‐time capture‐recapture in closed populations”," Biometrics, The International Biometric Society, vol. 76(3), pages 1034-1035, September.
    6. Wen-Han Hwang & Jakub Stoklosa & Ching-Yun Wang, 2022. "Population Size Estimation Using Zero-Truncated Poisson Regression with Measurement Error," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 303-320, June.

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