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Capture-recapture estimation using finite mixtures of arbitrary dimension
Richard Arnold1, Yu Hayakawa, Paul Yip
1School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, New Zealand. richard.arnold@vuw.ac.nz
Biometrics
|June 16, 2009
Summary
Bayesian capture-recapture models using reversible jump Markov chain Monte Carlo (RJMCMC) methods effectively handle population heterogeneity. This approach enables automatic model selection and provides stable population size estimates, outperforming traditional methods.
Area of Science:
- Ecology
- Statistics
- Computational Biology
Background:
- Capture-recapture models are crucial for estimating population sizes.
- Accounting for heterogeneity in capture probabilities is essential for accurate estimates.
- Traditional methods struggle with overparameterized models and heterogeneity.
Purpose of the Study:
- To introduce and demonstrate reversible jump Markov chain Monte Carlo (RJMCMC) methods for Bayesian capture-recapture models.
- To incorporate individual and sample heterogeneity into capture-recapture analyses.
- To showcase the advantages of RJMCMC over likelihood-based methods.
Main Methods:
- Fitting Bayesian capture-recapture models using RJMCMC.
- Incorporating heterogeneity via finite mixtures and fixed sample effects.
- Utilizing priors to stabilize parameter estimates and improve model performance.
Main Results:
- RJMCMC facilitates automatic model selection and model averaging.
- The method produces realistic credible intervals for population size, even with overparameterized models.
- Demonstrated effectiveness on ecological (Snowshoe hare, Cottontail rabbit) and reliability testing datasets.
Conclusions:
- RJMCMC offers a robust framework for fitting complex capture-recapture models.
- This Bayesian approach overcomes limitations of likelihood-based methods in handling heterogeneity and model selection.
- The method provides reliable population size estimations in diverse ecological and reliability contexts.
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