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A general class of recapture models based on the conditional capture probabilities

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  • A. Farcomeni

Abstract

type="main" xml:lang="en"> We propose an M hotb model for population size estimation in capture-recapture studies. The tb part is based on equality constraints for the conditional capture probabilities, leading to an extremely rich model class. Observed and unobserved heterogeneity are dealt with by means of a logistic parameterization. In order to explore the model class, we introduce a penalized version of the likelihood. The conditional likelihood and penalized conditional likelihood are maximized by means of efficient EM algorithms. Simulations and two real data examples illustrate the approach.

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  • A. Farcomeni, 2016. "A general class of recapture models based on the conditional capture probabilities," Biometrics, The International Biometric Society, vol. 72(1), pages 116-124, March.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:1:p:116-124
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    Cited by:

    1. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    2. Danilo Alunni Fegatelli & Luca Tardella, 2016. "Flexible behavioral capture–recapture modeling," Biometrics, The International Biometric Society, vol. 72(1), pages 125-135, March.
    3. Yauck, Mamadou & Rivest, Louis-Paul, 2019. "On the estimation of population sizes in capture–recapture experiments," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 512-524.
    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. Yuzi Zhang & Howard H. Chang & Qu Cheng & Philip A. Collender & Ting Li & Jinge He & Justin V. Remais, 2023. "A hierarchical model for analyzing multisite individual‐level disease surveillance data from multiple systems," Biometrics, The International Biometric Society, vol. 79(2), pages 1507-1519, June.
    6. Farcomeni, Alessio & Dotto, Francesco, 2021. "A correction to make Chao estimator conservative when the number of sampling occasions is finite," Statistics & Probability Letters, Elsevier, vol. 176(C).

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