Electronic Journal of Statistics

Identifiability and inferential issues in capture-recapture experiments with heterogeneous detection probabilities

Alessio Farcomeni and Luca Tardella

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We focus on a capture-recapture model in which capture probabilities arise from an unspecified distribution $F$. We show that model parameters are identifiable based on the unconditional likelihood. This is not true with the conditional likelihood. We also clarify that consistency and asymptotic equivalence of maximum likelihood estimators based on conditional and unconditional likelihood do not hold. We show that estimates of the undetected fraction of population based on the unconditional likelihood converge to the so-called estimable sharpest lower bound and we derive a new asymptotic equivalence result. We finally provide theoretical and simulation arguments in favor of the use of the unconditional likelihood rather than the conditional likelihood especially when one is willing to infer on the sharpest lower bound.

Article information

Electron. J. Statist., Volume 6 (2012), 2602-2626.

First available in Project Euclid: 11 January 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G10: Hypothesis testing
Secondary: 62F12: Asymptotic properties of estimators

Binomial mixture capture-recapture identifiability conditional likelihood complete likelihood unconditional likelihood


Farcomeni, Alessio; Tardella, Luca. Identifiability and inferential issues in capture-recapture experiments with heterogeneous detection probabilities. Electron. J. Statist. 6 (2012), 2602--2626. doi:10.1214/12-EJS758. https://projecteuclid.org/euclid.ejs/1357913090

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